Risk in Islamic Banking
Pejman Abedifar, Philip Molyneux, Amine Tarazi
To cite this version:
Pejman Abedifar, Philip Molyneux, Amine Tarazi. Risk in Islamic Banking. Review of Finance, 2013,
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Risk in Islamic Banking1
Pejman Abedifar*, Philip Molyneux†c, Amine Tarazi*
* Université de Limoges, LAPE, 5 rue Félix Eboué, 87031 Limoges, France
† Bangor Business School, Bangor University, Wales, LL57 2DG, UK
3 May 2012
Abstract
This paper investigates risk and stability features of Islamic banking using a sample of 553
banks from 24 countries between 1999 and 2009. Small Islamic banks that are leveraged or
based in countries with predominantly Muslim populations have lower credit risk than
conventional banks. In terms of insolvency risk, small Islamic banks also appear more stable.
Moreover, we find little evidence that Islamic banks charge rents to their customers for
offering Shariá compliant financial products. Our results also show that loan quality of Islamic
banks is less responsive to domestic interest rates compared to conventional banks.
JEL Classifications: G21; G32
Keywords: Islamic banking, Islamic finance, bank risk, credit risk, stability, insolvency, Zscore,
rent-seeking.
c Corresponding Author. Tel: +441248382170
E-mail addresses: pejman.abedifar@etu.unilim.fr, p.molyneux@bangor.ac.uk, amine.tarazi@unilim.fr.
1
An earlier version of this paper was circulated under the title "Risk and Stability in Islamic Banking". Thanks to
Robert DeYoung, Thorsten Beck, Shahid Ebrahim, Franco Fiordelisi, John Goddard, Laetitia Lepetit, Steven
Ongena, Philippe Rous, John Thornton, John Wilson and delegates at the European Central Bank’s Workshop on
Islamic Finance and Financial Stability, the International Workshop on Economic and Financial Risks Niort, IFABS
Rome, FMA Denver Conferences and the Wolpertinger Valencia Seminar for constructive comments on earlier
versions of the paper. All errors, of course, rest with the authors.
1
Introduction
The world has observed various evolutionary stages in the field of banking and currently
we see substantial growth in Islamic modes of banking and finance. According to TheCityUK
(2011) the assets of Islamic banks (including the Islamic windows of conventional banks)
increased to $1,041bn at the end of 2009 from $947bn in 2008. This is expected to have grown
by 10-15% during 2010 amounting to around 1.5% of global financial assets (Financial Times,
2011). The share of Muslims in the World’s population2 also suggests greater potential for this
type of financial activity in the future. Islamic banking has also experienced more rapid growth
than conventional banking post-2008 crisis (Hasan and Dridi, 2010), has expanded outside the
Muslim world to other continents including Europe and the Americas, and is continuing to
develop a broad array of innovative solutions to meet Islamic financing demands (for instance,
Shariá compliant credit default swaps). In line with these recent developments, the literature has
grown rapidly, mirroring the growth of Islamic finance itself.
Islamic financial principles have evolved on the basis of Shariá law, which forbids
payment or receipt of Riba – the payment or receipt of interest (Obaidullah, 2005). Financing
principles are governed by Islamic rules on transactions “Figh Al-Muamelat” and follow both
Profit and Loss Sharing (PLS) and non-PLS arrangements (such as leasing contracts). In addition
to the prohibitions on interest, Islamic banks also face other restrictions – such as the use of many
derivatives products, because according to Shariá all contracts should be free from excessive
uncertainty “Gharar” (Obaidullah, 2005)3.
2
Muslims represent around 23% of the world population as reported by Pew Research Center (2009).
Islamic derivative products that are permissible include: spot commodity and money transactions (where exchange
takes place contemporaneously or is deferred - the commodity is delivered at t+0 and the money delivered at t+1),
and Salam contracts (where money is paid at t+0 and the commodity delivered at t+1). There is widespread debate as
to whether Futures transactions (where money and commodity payments / deliverables are deferred) are Islamic.
3
2
Several papers have outlined the specific risks inherent in Islamic banking. Errico and
Farahbakhsh (1998) for instance point out that prudential supervision and regulations governing
Islamic banks should place a greater emphasis on operational risk and information disclosure.
They explain the special risks attached to PLS. For instance, in certain cases Islamic banks cannot
mitigate credit risk by demanding collateral from clients, as their relationship is established on
the basis of partnership; moreover, they do not have enough control over the management of
projects financed in the form of Mudarabah. Khan and Ahmad (2001) claim that sharing Islamic
banks’ profit or loss with their investment account holders introduces withdrawal risk. They also
argue that different Islamic modes of finance have their own unique risk characteristics due to the
various constraints enforced by Shariá (Islamic rules). Sundararajan and Errico (2002) suggest
that the complexities of PLS modes of finance and the risks associated with the non-PLS
activities should be taken into account to establish more effective risk management. They also
point out various moral hazard issues that occur as a result of the special relationship between
Islamic banks and investment account holders. Obaidullah (2005) argues that (deposit)
withdrawal risk may persuade Islamic banks to deviate from traditional Shariá financing
principles. This occurs if banks pay competitive market returns to investment account holders
regardless of the bank’s actual performance.
Table I provides a summary of empirical literature on Islamic banking where some of the
aforementioned issues are analyzed. Early empirical work focuses on the efficiency and
production technology features of banks (El-Gamal and Inanoglu, 2002; Yudistra, 2004) whereas
more recent studies examine competition (Chong and Liu, 2009; Weill 2011), asset quality (Beck
et al, 2010), stability (Čihák and Hesse 2010) and other risk dimensions including loan default
rates (Baele et al, 2010). Apart from some notable exceptions, the empirical literature suggests no
3
significant differences between Islamic and conventional banks in terms of their efficiency,
competition and risk attributes.
[TABLE I]
An interesting and related dimension focuses on the disciplinary role of depositors and
whether this is influenced by the religiosity of Islamic bank customers. Banking theory (Diamond
and Rajan, 2000 and 2001) points out that the discipline imposed by depositors mitigates risky
bank lending. In the context of Islamic banking the PLS relationship between the bank and
investment account holders, however, appears less clear-cut than in conventional banking.
Previous literature (such as Miller and Hoffmann, 1995 and Osoba, 2003) claims that religious
people are more risk averse so Islamic bank depositors may be more sensitive to bank
performance and demonstrate greater withdrawal risk than those at conventional banks.
Alternatively, they may show loyalty (for religious reasons) towards their bank and thus mitigate
the discipline exerted by withdrawal risks. In addition, Islamic bank clients may also be prepared
to pay rents for receiving financial services compatible with their religious beliefs.
This paper contributes to the most recent literature by investigating bank credit and
insolvency risk4 for a sample of Islamic banks, conventional banks with Islamic windows
(hereafter referred to as Islamic window banks) and traditional commercial banks from 24
member countries of OIC over 1999 to 2009. We also explore whether Islamic banks exploit the
religiosity of their customers by extracting rents (higher loan or lower deposit rates) for offering
Shariá compliant products and services.
4
In this paper, we are interested in bank risk at the individual level, rather than systemic risk. Typically, the
countries where our sample of Islamic and non-Islamic banks are based did not experience the credit crisis of 2008
onwards. These economies are also less leveraged than Western systems. For example, according to the World Bank
web-site, in the U.S. domestic credit provided by the banking sector is estimated at around 219% of GDP between
1999 and 2009, compared with about 50% for the countries under study in this paper.
4
Overall we find that Islamic banks have lower credit risk than conventional banks,
specifically small, leveraged or those operating in countries with more than 90% Muslim
populations. In terms of insolvency risk small Islamic banks are more stable than small
conventional banks, as they are more capitalized; however, no significant difference between
large Islamic and conventional banks is observed. Loan quality, (implicit) interest income and
expense of Islamic banks are less sensitive to domestic interest rates compared to their
conventional counterparts; however, the sensitivity of Islamic banks’ stability to interest rates is
not significantly different from conventional banks. Finally, we find no evidence that Islamic
banks charge rents to their clients for offering Shariá compliant financial products. The paper is
organized as follows. Section 1 discusses the key features of Islamic finance and risk issues and
Section 2 outlines our methodology. Section 3 describes the data and Section 4 presents the
results. Finally, section 5 concludes.
1. Background on Islamic Banking
This section briefly explains the key features of Islamic finance and its possible impact on
the risk and stability of banks.
1.1. FEATURES OF ISLAMIC FINANCE
Islamic finance is based on Shariá principles which forbid payment or receipt of Riba5.
Riba refers to an excess to be returned on money lending. The Islamic terminology for such a
kind of lending is “Qard Al-Hasan”. It is interesting to note that Shariá recognizes the time value
of money, since according to Islamic rules the price of a good to be sold on a deferred payment
5
There are two types of Riba: Riba in debt and Riba in exchange. For more details see Obaidullah (2005). This paper
focuses only on Riba in debt.
5
basis can be different from its current value. Interest reflects the time value of money and the
interest rate is an exchange rate across time. While Shariá recognizes interest in business it
prohibits interest on lending (Obaidullah, 2005).
Islamic finance has evolved on the basis of Islamic rules on transactions, Figh alMuamalat, and can mainly be categorized as: 1) Debt-based financing: the financier purchases or
has the underlying assets constructed or purchased and then this is sold to the client. The sale
would be on a deferred-payment basis with one or several installments. 2) Lease-based financing:
the financier purchases or has the underlying assets constructed or purchased and then rents it to
the client. At the end of the rental period (or proportionate to the rentals) ownership would be
transferred wholly or partially to the client. 3) PLS financing: the financier is the partner of the
client and the realized profit or loss would be shared according to pre-agreed proportions (Khan
and Ahmed, 2001). The first two Islamic finance methods are collectively known as Non-Profit
and Loss Sharing “Non-PLS”. Besides restrictions on Riba, Shariá has various other prohibitions
which should be taken into account. For instance, according to the Shariá all contracts should be
free from excessive uncertainty “Gharar” (Obaidullah, 2005); hence as noted earlier, Islamic
financial institutions face some restrictions on application of financial derivatives and other types
of contracts (including various forms of insurance policies).
1.2. ARE ISLAMIC BANKS RISKIER THAN CONVENTIONAL BANKS?
In this section, the asset and liabilities structure of Islamic banks are analyzed
highlighting their specific risk features.
6
1.2.1. Liabilities
Islamic banks are authorized to receive deposits mainly in the following two forms (Iqbal,
et al., 1998): current accounts6 that bear no interest but are obliged to pay principal to holders on
demand, and investment (or savings) accounts that generate a return based on profit rates. Such
rates may be adjusted according to the realized profit or even loss which would then be shared
between the Islamic bank and the investment account holders. This PLS arrangement can (in
theory at least) provide pro-cyclical protection to banks in the event of adverse conditions – profit
rates decline in bad times and increase in good times. The extent to which investment deposits are
important as a source of funding, therefore, can have an impact on the asset portfolio of Islamic
banks.
Due to the obligations towards depositors as debt-holders, conventional banks aim to
allocate a part of their funds to liquid assets, and endeavor to decrease the volatility and
uncertainty of loan revenues so as to meet depositor obligations. Islamic banks, however, have
more flexibility, since they can consider investment depositors more like equity holders.
However, this flexibility may be mitigated by the fact that Islamic banks have limited access to
wholesale funding. There is a fledgling Islamic money market (noticeably in Bahrain and
Malaysia) although only the largest institutions have access. As such, Islamic banks are rather
constrained from engaging in active liability management like conventional banks.
Calomiris and Kahn (1991) and Jeanne (2000) argue that short-term debt is useful in
disciplining financial intermediaries. Diamond and Rajan (2000, 2001) also show that the issue of
demand deposits encourages banks to monitor their lending activities. They also claim that a bank
run is the Nash equilibrium for individual depositors, although in the case of a run they may
collectively receive less than originally promised. In Islamic banking the payoff to investment
6
Deposits are received by Islamic banks in the form of “Qard Al-Hasan” or “Amanaa”.
7
account holders is contingent on both the performance of the bank as well as the religiosity of
depositors. This can result in an ambiguous outcome – religious depositors may be more loyal
and prepared to take lower returns, refusing (or at least stalling) from withdrawing deposits even
if the performance of the bank deteriorates. Alternatively, religious depositors may be more risk
averse showing greater sensitivity to bank’s performance and demanding higher returns. In such a
case investment account funding may be more fragile than time deposits, imposing greater
discipline on Islamic banks.
The case where religious factors lead to lower withdrawal risk for investment account
holders may influence Islamic banks’ lending behavior. It may weaken their incentives for due
diligence and loan monitoring, since Islamic banks can transfer credit risk to investment account
holders who do not have the same rights as equity holders but share the same risk (Sundararajan
and Errico, 2002). Alternatively, the special relationship can discipline Islamic banks more
effectively (compared to conventional banks) since investment accounts holders have greater
incentives to monitor Islamic bank performance. In such a case, Islamic depositors are more
likely to shift their deposits from poor-performing banks to those offering higher returns or even
to conventional banks. Hence, there could be greater potential for withdrawal risk (Khan and
Ahmed, 2001) and as such depositors can discipline Islamic banks more actively.
Sharing the realized profit or loss with investment account holders may make Islamic
banks more risky. On the upside, larger payouts to investment account holders may increase
deposits and this can force bank shareholders to raise more equity capital in order to maintain
capital ratios and prevent dilution of their ownership rights. Conversely, poor payouts may
encourage deposit withdrawals leading to potential liquidity and (ultimately) solvency problems.
8
1.2.2. Islamic Banking: Principles and Practice
Islamic banks, in practice, tend to deviate somewhat from the above mentioned financing
principles and can operate similarly to conventional banks. Obaidullah (2005) claims that
withdrawal risks may persuade management to vary from PLS principles by paying competitive
market returns to investment account holders regardless of realized performance. Chong and Liu
(2009) use Malaysian data to show that investment deposit rates of Islamic banks are closely
linked to those of their conventional counterparts. They argue that competitive pressure from
conventional banks constrains the actual implementation of PLS arrangements. This strategy can
also help management to mitigate the sensitivity of investment account holders to bank’s
performance and hence avoid greater discipline.
In other words, equity-holders of Islamic banks can be at risk from transferring a part of
their profits to investment account holders so as to reduce withdrawal risk. Such a risk is known
as Displaced Commercial Risk (AAOIFI, 1999). Nevertheless, in the likelihood of crisis,
management is highly likely to share realized losses with investment account holders to avoid
insolvency. This suggests that Islamic banks may have a greater capacity to bear losses compared
to conventional banks. The magnitude of the extra capacity depends on the weight of investment
deposits in total funding. When Islamic banks are performing well they may adjust profit rates
upward but at a slower rate than realized profitability so as to limit the level and volatility of
deposit inflows.
Implicitly, investment account holders own a bond, a long position on a call option and a
short position on a put option. The strike price of the call, however, is determined arbitrarily by
Islamic banks, in the absence of supportive regulations on the account holders’ rights. The strike
price of the put is determined based on the degree of market competitive pressures, level of
incurred loss and the capital ratio of the Islamic bank. Figure (1) illustrates how the special
9
relationship between investment account holders and an individual Islamic bank works in theory
and practice compared to holders of time deposits in a typical conventional bank.
[FIGURE 1]
1.2.3. Assets
In the process of lending, Islamic banks tend to apply non-PLS principles due to the risks
and complexities associated with the PLS method. For instance, under PLS financing, Islamic
banks need to determine the profit or loss sharing ratio for each project which can be complicated
due to difficulties in quantifying the characteristics of clients and the proposed business
opportunity. Revenue is not guaranteed and since they cannot collect collateral, they need to put
more effort into selection and monitoring so as to ensure that informational rents are not extracted
by borrowers. Hence, for short-term financing, it is not viable for Islamic banks to use the PLS
method. Moreover, under the Mudarabah contract, Islamic banks have limited means to control
and intervene in the management of a project7.
Aggarwal and Yousef (2000) find that Islamic banks mainly use Non-PLS instruments to
avoid the moral hazard problem associated with PLS financing. Chong and Liu (2009) show that
in Malaysia, only 0.5% of Islamic bank finance is based on PLS principles. Dar and Presley
(2000) claim that even Mudarabah companies in Pakistan, which are supposed to operate in the
form of PLS mainly follow Non-PLS modes of finance. This is also emphasized by Baele et al
(2010). According to Bank Indonesia (2009) PLS modes of finance accounted for 35.7% in the
financing of Islamic banks operating in the country by the end of 2008. The report points out that
the use of the PLS method in Indonesia is among the highest compared to what is practiced in
other countries. Mills and Presley (1999) also claim that PLS is only marginally practiced in
7
Errico and Farahbakhsh (1998), Dar and Presley (2000) and Sundarajan and Errico (2002) discuss the complexity
of the PLS method.
10
Bangladesh, Egypt, Iran, Pakistan, Philippines and Sudan. However, while Islamic banks appear
to refrain from practicing PLS modes of finance they still face possible greater withdrawal risks
than conventional banks (Khan and Ahmad, 2001; and Sundararajan and Errico, 2002).
1.2.4. Complexity of Islamic Modes of Finance
Islamic financing agreements8, even for Non-PLS methods, are not as straightforward as
conventional loan contracts (and according to anecdotal evidence also take longer to process).
Generally, in debt-based or lease-based finance, such as Murabaha, Islamic banks arrange for the
goods/projects to be purchased and then sell or rent them to clients. For purchase/implementation
of the goods/projects, Islamic banks normally appoint the client as their agent. Such a framework
is somewhat complicated as compared to conventional loan contracts. Sundarajan and Errico
(2002) note the specific risks attached to various Non-PLS methods, such as Salam and Ijara. In
the former, Islamic banks are exposed to both credit and commodity price risks; in the latter,
unlike conventional lease contracts, Islamic banks cannot transfer ownership and therefore have
to bear all the risks until the end of the lease period.
Another area of debate relates to the treatment of default penalties. Some jurisdictions
rule that such penalties are not authorized by Shariá9, so banks make use of rebates instead (Khan
and Ahmed, 2001). Here the mark-up on the finance arrangement implicitly covers the return to
the banks as well as a default penalty component. If the client repays the loan in a timely manner
then they will receive the rebate. While default interest payments are typically calculated over the
8
See Khan (1991), Khan (1992), Ahmad (1993) and Iqbal and Mirakhor (2007) for details on the features of various
Islamic financial instruments.
9
Islamic scholars generally consider the default penalty as the interest on debt which is prohibited by Sharia as
explained in sub-section 1.1.; however, it is treated differently across countries. In Iran, for instance, default penalty
is a penalty for non-fulfillment of a commitment and it should not be classified as the interest on debt. In Pakistan,
Islamic experts have authorized the default penalty, only if it is spent on charity (Baele et al., 2010).
11
delayed period in conventional banking, some Islamic banks collect the delayed penalty over the
whole financing period. In addition, Islamic banks can also face restrictions regarding the use of
derivatives as well as different types of collateral, for instance, they are not authorized to use
interest-based assets, like bonds, for security (Khan and Ahmed, 2001).
1.2.5. Investment Limitations
In addition to lending, conventional banks also allocate a part of their funds to
investments. Such investments normally include purchase of bonds (as well as instruments with
shorter maturities) of different types that have risk/return features that help manage portfolio risk.
However, Islamic banks have limited options for such investments since they are not authorized
to invest in interest bearing instruments. Alternatively they can invest in Islamic bonds, known as
Sukuk10. Although (like in short-term Islamic money markets) this asset class still remains
relatively underdeveloped, limitations on Islamic bank investment opportunities have been
weakened over time due to the expansion of alternative Islamic financing instruments.
1.2.6. Relationship between Clients’ Risk Aversion and Religiosity
Since Islamic banking is characterized by observing Shariá requirements, clients with
religious beliefs are more likely to prefer Islamic to conventional banking. In a dual banking
system where both Islamic and conventional banking are practiced, the market is segmented:
religious clients may choose Islamic banking, while others might be indifferent between Islamic
and conventional banks. The existing literature shows a positive relationship between religiosity
and an individual’s risk aversion (Miller and Hoffmann, 1995; Osoba, 2003; Hilary and Hui,
10
They are similar in nature to debt certificates, and can only be issued on the basis of the revenue which is expected
to be generated by an underlying asset.
12
2009).We have already noted that religiosity may affect the bank’s lending from the liability side
through the disciplinary role of deposits. It can also influence the bank’s performance from the
asset side by encouraging borrowers to fulfill their obligations under Islamic loan contracts. All
in all, assuming all other factors equal, whether Islamic banks face more or less credit risk
compared to conventional banks is likely to be influenced by the religious features of the client
base.
Overall, Islamic banking is characterized by various features that appear on the one hand
to reduce credit risk. Greater discipline associated with higher deposits fragility (exerted by
depositors’ risk aversion) and the religious beliefs of borrowers may induce loyalty and
discourage default. On the other hand Islamic banks may face greater credit risk due a variety of
factors such as: the complexity of Islamic loan contracts, limited default penalties and moral
hazard incentives caused by PLS contracts. In terms of insolvency risk, the special relationship
with depositors could provide Islamic banks with greater capacity to bear losses yet at the same
time, operational limitations on investment and risk management activities could make them less
stable than their conventional counterparts. Also, while interest is forbidden in Islamic banking,
those institutions that compete with conventional banks may be forced to mirror their pricing
behavior and as such may be sensitive to interest rate changes. Whether they have higher or lower
sensitivity compared to conventional banks is an empirical question which we try to answer in
this paper. Specifically we are interested in investigating whether Islamic bank’s credit risk is
more or less responsive to interest rate movements, taking into account the (expected) higher risk
aversion of Islamic borrowers. We also examine the interest rate sensitivity of insolvency risk.
Understanding the risk features of Islamic versus conventional banks enables us to
investigate whether Islamic banks extract special rents from clients for offering financial products
that are compatible with their religious beliefs. Phrased differently, knowing the constraints of
13
religious clients, Islamic banks may charge higher rates to borrowers and give lower rates to
depositors. The extra rents would then be considered as the price of offering Shariá-compliant
products.
During the Islamic Finance World North America conference (Toronto-2007), it was
reported that at least one third of North American Muslims refuse conventional mortgages and
are willing to pay more for religiously sound products. In Canada, Islamic mortgages are between
100-300 basis points more expensive than conventional mortgages. In the U.S. the spread is 40 to
100 basis points11. Baele et al (2010) find that in Pakistan, the interest (mark-up) rate is, on
average, two percentage points higher for Islamic than for conventional loans, even though the
default probability of the former is lower. However, Weill (2011), using a sample of 1,301
observations for 34 Islamic and 230 conventional banks operating in 17 OIC member countries
between 2001 and 2007, computes Lerner indices and finds that Islamic banks have lower price
mark-ups (market power) than conventional banks.
2. Methodology and Econometric Specifications
Our methodology compares the risk features of Islamic and conventional banks while
controlling for a variety of potentially influential factors. A similar approach is used to
investigate whether Islamic banks extract special rents from their clients. We believe that Islamic
and traditional banks can be compared as previous literature (Chong and Liu, 2009) has found
that the former can mimic the latter in terms of financial behavior notwithstanding operational
differences (Islamic contracts, PLS arrangements and so on) that can cause risk divergence. The
following three model specifications are estimated:
11
See
http://www.canada.com/nationalpost/financialpost/story.html?id=01ff2407-f4fe-4c16-80ad1172d0d25763&k=5052.
14
Credit_Riski,t = α0 + α1×Islamic_Banki,t + α2×Islamic_Window_Banki,t + α3×Sizei,t-1 +
(1)
α4×Market_Sharei,t-1 + α5×Capital_Asset_Ratioi,t-1 + α6×Loan_Growthi,t-1 +
α7×Noninterest_Incomei,t-1 + α8×Cost_Inefficiencyi,t-1 + α9×State_Banki,t + α10×Foreign_Banki,t +
α11×Subsidiaryi,t + α12×Young_Banki,t + α13×Middle_Aged_Banki,t + α14×Muslim_Sharei +
α15×Domestic_Interest_Ratei,t-1 + α16×HHIi,t-1 +α17×GDP_Per_Capitai,t-1 + α18×GDP_Per_Capita_Growthi,t-1 +
∑ α, × Year_Dummies, + ∑
α, × Country_Dummies, + εi,t
Insolvency_Riski,t = β0 + β1×Islamic_Banki,t + β2×Islamic_Window_Banki,t + β3×Sizei,t-1 +
(2)
β4×Market_Sharei,t-1 + β5×Loan_Total_Earning_Asset_Ratioi,t-1 + β6×Asset_Growthi,t-1 +
β7×Noninterest_Incomei,t-1 + β8×Cost_Inefficiencyi,t-1 + β9×State_Banki,t + β10×Foreign_Banki,t +
β11×Subsidiaryi,t + β12×Young_Banki,t + β13×Middle_Aged_Banki,t + β14×Muslim_Sharei +
β15×Domestic_Interest_Ratei,t-1 + β16×HHIi,t-1 + β17×GDP_Per_Capitai,t-1 + β18×GDP_Per_Capita_Growthi,t-1 +
∑ β, × Year_Dummies, + ∑
β, × Country_Dummies, + ƞi,t
Bank_Interest_Ratei,t = γ0 + γ1×Islamic_Banki,t + γ2×Islamic_Window_Banki,t + γ3×Sizei,t-1 +
γ4×Market_Sharei,t-1 + γ5×Capital_Asset_Ratioi,t-1 + γ6×Noninterest_Incomei,t-1 + γ7×Cost_Inefficiencyi,t-1 +
γ8×Credit_Riski,t-1 + γ9×State_Banki,t + γ10×Foreign_Banki,t + γ11×Subsidiaryi,t + γ12×Young_Banki,t +
γ13×Middle_Aged_Banki,t + γ14×Muslim_Sharei + γ15×Domestic_Interest_Ratei,t-1 + γ16×HHIi,t-1 +
γ17×GDP_Per_Capitai,t-1 + γ18×GDP_Per_Capita_Growthi,t-1 + ∑ γ, × Year_Dummies, + ∑
γ, ×
Country_Dummiesi,c + Ɵi,t
(3)
Where i subscripts denote individual banks and t denotes the time dimension. Credit risk,
insolvency risk and bank interest rates are modeled in Equations (1) to (3), respectively. Credit
risk relates to loan quality, insolvency risk represents a bank’s stability and the interest rate
model is expected to capture any special rents extracted by Islamic banks from their clients.
The first and second Equations enable us to compare credit and insolvency risks of
Islamic versus conventional banks, using a dummy variable which takes the value of one when a
bank is Islamic and zero otherwise (Islamic_Bank). Islamic window banks are also represented
by a dummy variable (Islamic_Window_Bank)12. Hence, conventional banks are considered the
benchmark. The third Equation aims to investigate whether Islamic banks charge rents (compared
to conventional banks) for their Shariá compliant services. Simply, Equation (3) analyzes the
determinants of a range of interest rate measures (net interest margin, interest income and interest
12
Controlling for Islamic window banks enables us to compare fully Islamic versus fully conventional banks. It
would have been interesting to compare the credit risk of conventional and Islamic windows of the same bank, but
due to data unavailability this was not feasible.
15
expense – including Islamic equivalents) to test for Islamic bank rent seeking behavior13. Higher
net interest margin or implicit interest income rates on loans (or lower implicit interest expense
on deposits) would suggest that Islamic banks extract rents from their clients for offering Islamic
products/services.
The established literature shows that interest rate changes can affect banks’ soundness
through changes in banks’ asset quality (Jarrow and Turnbull, 2000; Carling et al., 2007;
Drehmann et al., 2010 and Alessandri and Drehmann, 2010). In our analysis, therefore we study
the influence of domestic interest rates through three channels: its impact on credit risk (Equation
1), insolvency risk (Equation 2) and on various bank-level interest rate prices (Equation 3). For
the first channel, we include interest rates in our model of credit risk and also add an interest rate
and Islamic bank dummy interaction term – this shows the sensitivity of Islamic banks’ credit
risk to interest rate variation. The second channel (Equation 2) explores whether the insolvency
risk of Islamic banks has a different sensitivity to interest rates compared to conventional banks.
For the third channel, we examine the determinants of a variety of bank-level interest rate
(implicit and explicit) prices including: net interest margin, interest income and expense (as well
as loan and deposit rates) using a set of controls and explanatory variables. Similar to the
previous channels, the interaction term of our interest rate variable and the Islamic bank dummy
shows whether earnings and expenses of Islamic banks are more or less exposed to interest rate
variation than their conventional counterparts. The model also tests for possible rent seeking
behavior in Islamic banking using the variety of implicit and explicit interest rate dependent
variables and the Islamic bank dummy.
13
The specification of Equation 3 is based on the bank interest margin literature (Angbazo, 1997; Wong, 1997;
Maudos and De Guevara, 2004; Carbo and Rodriguez (2007) and Lepetit et al. (2008b).
16
2.1. DEPENDENT VARIABLES
We primarily use the ratio of loan-loss reserves to gross loans (Loan_Loss_Reserve) as a
proxy for credit risk (Credit_Risk). This variable represents managers’ assessment of the quality
of the loan portfolio, including performing and non-performing loans. Loan_Loss_Reserve takes
into account the past performance and the expectation for future performance of the existing loan
portfolio (a bank may have lower non-performing loans simply because the repayment period of
the major part of its loan portfolio has not yet started). Its periodic adjustment is reflected in the
income statement in the form of loan loss provision. As a robustness check we also employ the
ratio of impaired loans to gross loans (Impaired_Loans) and the ratio of loan-loss provisions to
average gross loans (Loan_Loss_Provision) both backward-looking proxies for credit risk. All
three proxies represent the quality of bank’s existing loans and are widely used in the empirical
banking literature (for instance, Angbazo, 1997; Kwan and Eisenbeis, 1997; Shiers, 2002;
Konishi and Yasuda, 2002; Cebenoyan and Strahan, 2004; Gonzalez, 2005; Altunbas et al., 2007
and Lepetit et al., 2008a). It should be noted, however, that these indicators of credit risk only
partly reflect the quality of the loan portfolio, since variation across banks may be due to different
internal policies regarding problem loan classification, reserve requirements and write-off
policies.
For insolvency risk analysis, we employ the Zscore measure which is widely used in the
literature as a stability indicator (see, for instance, Goyeau and Tarazi, 1992; Boyd and Runkle,
1993; Lepetit et al., 2008a; Hesse and Čihák, 2007; Čihák et al., 2009; Laeven and Levine, 2009;
Čihák and Hesse, 2010). Using accounting information on asset returns, its volatility and
leverage, the Zscore is calculated as follows: "#$%&' =
*+,-./0 1.,
23+,-./
, where E(ROA) is the
expected return on assets, CAR is the ratio of equity capital to assets and SD(ROA) is the
17
standard deviation of ROA. Zscore is inversely related to the probability of a bank’s insolvency.
A bank becomes insolvent when its asset value drops below its debt. The insolvency probability
can be written as P(ROA<-CAR). If we use the standardized ROA, the probability would be
,-.6*+,-./
equal to 4 5
23+,-./
< −"#$%&'9. Hence the Zscore shows the number of standard deviation
that a bank’s return has to fall below its expected value to deplete equity and make the bank
insolvent. A higher Zscore implies that the bank is more stable. To control for outliers and
skewness of the distribution, we use the logarithm of the Zscore and its components.
Finally, we examine whether Islamic banks charge rents to their clients, in the form of
charging higher rates to borrowers or offering lower rates to depositors. First, we use net interest
margins (Net_Interest_Margin) that may capture rents collectively on both the loan and deposits
sides. As further robustness checks, we also use the implicit interest income rate
(Interest_Income_Rate), implicit interest expense rate (Interest_Expense_Rate), implicit interest
rate on loans (Loan_Rate) and the implicit interest rate on deposits (Deposit_Rate). It is worth
noting that while Islamic banks do not pay or earn interest, they do charge their clients a mark-up
which is equivalent (similar) to interest in conventional banking. Annex 1 defines our risk proxies
and control variables.
2.2. CONTROL VARIABLES
Islamic_Bank and Islamic_Window_Bank are dummies for Islamic banks and windows,
respectively. A variety of other control variables are included in the estimation of our models:
Size, Market_Share, Capital_Asset_Ratio, Loan_Total_Earning_Asset_Ratio, Loan_Growth,
Asset_Growth, Noninterest_Income and Cost_Inefficiency. We also control for:
18
•
Ownership structure, using three dummy variables: State_Bank, Foreign_Bank and
Subsidiary;
•
Bank age or experience level, using two dummies: Young_Bank and Middle_Aged_Bank;
•
Macroeconomic
indicators:
Muslim_Share,
Domestic_Interest_Rate,
HHI,
GDP_Per_Capita and GDP_Per_Capita_Growth;
•
Year and country dummies.
The rationale for their inclusion is set-out below.
The logarithm of total asset is considered as a proxy for size (Size). Large banks can
benefit from both scale economies and diversification as claimed by Hughes et al. (2001). At the
same time, larger banks might be more risky, since they may try and exploit Too-Big-To-Fail
safety net subsidies (Kane, 2010). Market share measured as bank assets over total banking sector
assets (Market_Share) is used as the proxy for market power (as in Berger, 1995).
The share of equity capital in total assets (Capital_Asset_Ratio) is included in the first
(Credit_Risk) and the third (Bank_Interest_Rate) Equations14. We include Capital_Asset_Ratio in
the credit risk Equation, since on the one hand, an increase in equity can lower moral hazard
problems and increase the monitoring incentives of banks (Diamond, 1984). On the other hand,
higher equity can increase banks’ risk-taking capacity. This variable is included as it allows us to
investigate whether the relationship between equity capital and risk varies between Islamic and
conventional banks. Capital_Asset_Ratio is also used in the Bank_Interest_Rate Equation, as
previous studies on the determinants of margins suggest a positive relationship (Carbo and
Rodriguez, 2007). Equity can be considered as a risk aversion proxy (McShane and Sharpe, 1985
and Maudos and De Guevara, 2004) and banks with higher equity expect higher returns.
14
This is not incorporated in the second equation, since our insolvency risk proxy accounts for the degree of
leverage.
19
Islamic banks can have various limitations in their investment of other earning assets
(section (1.2.5.)) which may adversely affect their stability. Hence, we include the share of net
loans in total earning assets (Loan_Total_Earning_Asset_Ratio) in the second model to
investigate the extent to which the composition of total earning assets impacts on insolvency risk.
The growth rate of gross loans (Loan_Growth) is controlled for in the credit risk
Equation since a considerable increase in credit may reflect weaker screening standards, relaxed
collateral requirements or lower interest rates (Dell’Ariccia and Marquez, 2006; Ogura, 2006).
Clair (1992) finds a negative effect of credit expansion on non-performing loans and loan chargeoff rates, although for subsequent years a positive link is detected. As pointed out by Berger and
Udell (2004) and Foos et al. (2010) borrowers do not default immediately after taking-on loans.
For insolvency risk analysis, as we need to take into account the growth strategy of banks, we use
total asset growth (Asset_Growth) in lieu of loan growth.
Share of non-interest income in total operating income (Noninterest Income) and cost
inefficiency are included in all three models. A bank may lose its focus on loan activity as it
moves towards noninterest income businesses. Alternatively, the expanding scope of activities
may improve a bank’s position in lending as it can collect valuable information from different
business lines that can be used for lending. According to previous studies, an increase in the share
of non-interest income in total operating income is expected to lower stability. DeYoung and
Roland (2001) and Stiroh (2004, 2006, 2010), for instance, claim that the increased reliance on
non-interest income has raised the volatility of bank portfolios without increasing average profits.
Lepetit et al. (2008a) show that European banks with a higher non-interest income share in their
net operating income, exhibit a higher insolvency risk. The share of noninterest income in total
operating income is also included in the third Equation, as Carbo and Rodriguez (2007) and
Lepetit et al. (2008b) show that noninterest income enables banks to lower margins.
20
Kwan and Eisenbeis (1997) show that inefficiency increases bank risks – illustrating
moral hazard that poorly-run banks have greater incentives for risk-taking. Hence, we control for
cost inefficiency (Cost_Inefficiency) using the cost to income ratio in our credit and insolvency
Equations15. A bank with greater cost inefficiency needs to have higher net interest margins to
compensate for losses incurred due to inefficiency. Thus, Cost_Inefficiency is included in the
Bank_Interest_Rate Equation. In the third Equation, we also control for credit risk, using the
Loan_Loss_Reserve proxy, which has been found to be a determinant of interest margins
(Angbazo, 1997; Wong, 1997; Maudos and De Guevara, 2004; Carbo and Rodriguez, 2007).
Bank ownership structure should also be taken into account. La Porta et al. (2002) analyze
government ownership of large banks in 92 countries and show that it reduces efficiency. Bonin
et al. (2005) investigate the impact of ownership on bank efficiency for eleven transition
countries and find that foreign-owned banks are more cost efficient than other banks. Iannotta et
al. (2007) using a sample of 181 large banks from 15 European countries claim that state-owned
banks have poorer loan quality and higher insolvency risk than other types of banks16. In our
model, we classify banks into four categories17: domestic privately-owned banks, domestic state-
15
See Mohamad et al. (2008) for a cross-country study of Islamic versus conventional banks using the stochastic
frontier approach and a sample of 37 conventional and 43 Islamic banks operating in 21 OIC member countries for
the 1990-2005 period. They find no significant difference in terms of efficiency between Islamic and conventional
banks; however, Abdul-Majid et al. (2010) apply a distance function approach and find that Islamic banks are less
technically efficient than their conventional counterparts. They use a sample of 558 observations covering 23 Islamic
and 88 conventional banks that operate in 10 OIC member countries over 1996 and 2002. Beck et al (2010) use more
conventional measures of bank efficiency – overhead costs and the cost-to-income ratio. Starting with a sample of
2,956 banks (of which 99 are Islamic) from 141 countries between 1995 and 2007. Islamic banks appear more
efficient than their conventional counterparts. However, when they examine data from the 22 countries where
Islamic and conventional banks compete together they find that Islamic banks have significantly higher overhead
costs but only slightly higher cost to income ratio compared to conventional banks.
16
For a discussion of empirical investigation of ownership issues in banking see Altunbas et al. (2001) and Goddard
et al. (2004). More recent studies include Barry et al. (2011), Taboada (2011), Forssbæck (2011) and Berger et al.
(2009).
17
We classify a bank as a state-owned bank when at least fifty percent of the equity belongs to the government.
Similarly, at least fifty percent of a bank should be owned by one or more foreign entity(ies) to be classified as a
foreign-owned bank. A bank which is owned by a foreign government is considered as a foreign-owned bank. We
21
owned banks (State_Bank), foreign-owned banks (Foreign_Bank) and subsidiaries (Subsidiary).
Domestic privately-owned banks are used as the benchmark and hence three dummies are
introduced to represent the other banks.
State-owned banks may invest in risky projects as a result of political influence, or/and
they may also enjoy some benefits and informational rents from political bodies. Foreign-owners
can face greater risk in monitoring the bank’s activities since they may be less familiar with the
legal and judicial setting in which they operate. Alternatively, due to such problems they may
pursue relatively conservative strategies. A subsidiary might structure a risky portfolio of loans,
simply because such a portfolio can beneficially contribute to diversification of the parent’s
overall portfolio. Failure of a subsidiary may not be viewed as undesirable in the event of a crisis
if reputational risks are low.
We also consider the age of the bank by defining two dummy variables. Banks with at
least three years of operation are categorized as young banks (Young_Bank) and those which
have been operating for a period ranging from three to seven years are considered as middle aged
(Middle_Aged_Bank). Other banks, called mature banks, are considered as the benchmark. The
age of banks is expected to proxy for experience and informational advantages. Older banks are
likely to have longer term relationships and other informational advantages (experience operating
in new geographies and product markets) that are reflected in efficiency and risk advantages. Of
course, it could be the case that younger institutions have tougher regulatory oversight and
therefore operate more cautiously.
We also introduce five country level variables to control for cross-country variations. First
we control for the degree of religiosity, using two interchangeable proxies: the share of Muslim
assume that although a government may decide to invest in a bank abroad based on political ties with the host
country, it will not intervene in the bank’s operation as intensively as the host country’s government.
22
population in each country (Muslim_Share) and an index representing the country’s legal system
(Legal_System). In the latter case, the index takes a value of zero for countries which do not use
Shariá law to define their legal system, a value of one for those countries that have legal systems
based on both Shariá and other legal traditions (such as English or French laws); and finally, the
index has a value of two for countries with exclusive Shariá based legal systems (such as Iran
and Saudi Arabia).
We also control for the level of domestic interest rates (Domestic_Interest_Rate). The
existing literature shows that the level of domestic interest rates can influence banks’ risk appetite
(Dell’ Ariccia and Marquez, 2006; Rajan, 2006; Borio and Zhu, 2008; Delis and Kouretas, 2010;
Maddaloni and Peydró, 2011). Typically, banks are found to have a higher risk-taking appetite
when interest rates are low. However, interest rate levels can influence the ability of borrowers to
re-pay (Jarrow and Turnbull, 2000; Carling et al., 2007; Drehmann et al., 2010 and Alessandri
and Drehmann, 2010) - at higher levels the incentive to default (moral hazard) increases. We try
to capture the possible impact of banking sector concentration on risk-taking behavior by
including the Herfindahl-Hirschman Index (HHI) in the model. Finally, we control for the level
and growth in the prosperity of the population by including the following variables - GDP per
capita (GDP_Per_Capita) and growth in GDP per capita (GDP_Per_Capita_Growth). Year
dummies are introduced to control for time fixed effects18 and we also include country dummies
to capture heterogeneity across different banking systems19.
18
The sample covers eleven years, however, since all accounting and macro level variables are lagged for one year,
we use nine year dummies (2001-2009) in our estimations.
19
This is particularly important due to differences in the nature of Islamic banking across countries. Unfortunately,
our data does not enable us to construct an index reflecting the degree of difference between Islamic and
conventional banks in each country. Nevertheless, we control for this dimension by introducing 23 country dummy
variables. It is worth noting that since Muslim_Share and Legal_System are time-invariant country level variables,
we use country dummies and Muslim_Share / Legal_System interchangeably to avoid perfect multi-collinearity.
23
3. Data and Descriptive Statistics
Bank-level data was retrieved from the Bankscope database and the web sites of
individual banks. Country-level variables, including domestic interest rate20, GDP per capita and
the growth rate of GDP per capita are collected from the World Bank web-site. The share of
Muslim population in each country is obtained from Pew Research Center21 and the data on legal
systems are obtained from the World Factbook. The Bankscope classification for Islamic banks is
incorrect in places so all banks have been cross-checked with their websites to ensure accuracy22.
The sample covers 3870 observations for 553 commercial banks, across 24 country23 members of
the OIC where Islamic banking is practiced over the period 1999 to 2009 (see Table A in Annex
2 for a detailed summary of cross-country and bank type specifications). Our sample comprises
118 Islamic commercial banks, 81 commercial banks with Islamic window/branches and 354
conventional commercial banks. For Iran, observations are only available for Islamic banks as its
banking system is 100% Riba-free. In other countries, both Islamic and conventional banking are
authorized and practiced. The largest number of observations is from Indonesia and the lowest
from Brunei. Approximately, 20% of the total observations are for Islamic banks; Islamic
window banks represent 17% of the sample (the remaining 63% relate to conventional banks).
Table B in Annex 2 shows the ownership structure and age (experience level) of banks in our
sample. The data reveal that Islamic banks are relatively younger than conventional banks and
20
We use deposit interest rate announced by the World Bank; for years and countries with missing observations, the
data is obtained from the web-site of central banks.
21
Please visit http://pewforum.org/Mapping-the-Global-Muslim-Population.aspx
22
Bankscope classifies banks as commercial, Islamic or other types. However an Islamic bank can be a commercial
or a non-commercial bank. Such a classification is problematic: (1) In Bankscope some Islamic banks are mistakenly
categorized as commercial banks. (2) Some Islamic banks are investment banks or other types that are not
comparable with commercial banks. (3) The data-set also does not differentiate conventional banks with Islamic
windows from Islamic or conventional banks.
23
Algeria, Bahrain, Bangladesh, Brunei, Egypt, Gambia, Indonesia, Iran, Iraq, Jordan, Kuwait, Lebanon, Malaysia,
Mauritania, Pakistan, Qatar, Saudi Arabia, Senegal, Syria, Sudan, Tunisia, Turkey, UAE and Yemen.
24
also the number with foreign owners is proportionately higher. Annex 3 also shows the
macroeconomic and banking indicators for the countries under study.
Table II illustrates sample descriptive statistics. It shows that relatively large conventional
banks establish Islamic windows. Islamic banks are, on average, more capitalized and profitable
than conventional banks. The lower levels of debt (possibly as a response to higher withdrawal
risk) and higher non-interest income of Islamic banks might partly explain their greater
profitability. Net interest margin of Islamic banks does not appear to be significantly different
from that of conventional banks; however, Islamic banks have lower implicit interest income and
expense rates than conventional banks. Interestingly, the structure of the asset portfolio of Islamic
banks is significantly different from that of conventional banks. Islamic banks have a higher ratio
of net loans to total earning assets possibly because they are limited in their investments in other
earning assets (such as bonds) as discussed in section (1.2.5.). Gross loans and total assets grow
at higher rates for Islamic than conventional banks. The cost to income ratio of Islamic banks is
slightly higher than that of conventional banks.
The descriptive statistics of our risk measures show that Islamic banks have lower levels
of credit risk compared to conventional banks. In terms of insolvency risk the mean test results
show that the Zscore and its components for Islamic banks are not significantly different from
those of conventional banks, suggesting that the higher returns and capital of Islamic banks are
offset by their higher asset return volatility.
[TABLE II]
A correlation matrix is presented in Annex 4 which does not suggest any major
collinearity problems among our independent variables, except for the logarithm of total assets
and market share variables. As a result, we orthogonalize the logarithm of market share on the
logarithm of total assets.
25
4. Empirical Results
4.1. CREDIT RISK
Table III presents the results for credit risk (Equation (1)) where we first use
Loan_Loss_Reserve as the credit risk proxy. The Equation is estimated using random effects24. In
column (1), the credit risk proxy is regressed simply on our Islamic bank and Islamic window
dummy variables (Islamic_Bank & Islamic_Window_Bank). Different classes of control
variables, including financial structure, ownership structure, age, macroeconomic indicators, year
and country dummies, are included in columns (2) to (6). These improve the explanatory power
of our model with R-squared increasing from 0.007 to 0.18225. In all specifications, Islamic
banks, on average, exhibit lower credit risk than conventional banks. The results remain
unchanged when we use Impaired_Loans and Loan_Loss_Provision as the credit risk proxies in
lieu of Loan_Loss_Reserve. As a further robustness check, we assume within country correlation
of standard errors, using clustered standard errors, and find similar results26. Islamic banks, on
average, hold 3.037% less reserves for their loans than conventional banks. The average loan-loss
reserves on gross loans for conventional banks is 8.72% (Table II) so Islamic banks hold 34.8%
(100 ×
.=
>.=
) less than the average Loan_Loss_Reserve that conventional banks hold.
Interestingly, the figure is close to Baele et al’s (2010) finding that the hazard rate of Islamic
loans is, on average, 33% lower than the hazard rate of conventional loans.
24
We have several dummy variables that rarely change over time, namely, Islamic_Bank, Islamic_Window_Bank,
State_Bank, Foreign_Bank and Subsidiary and so these variables have limited within variation. We also have time
invariant variables (Muslim_Share, for instance). Fixed effects estimation is inefficient at estimating variables with
limited within variance and cannot be used with time invariant variables. As such we employ the random effects
technique in our estimation.
25
The explanatory power of our models is close to similar studies, for instance Beck et al., 2009 and Čihák and
Hesse, 2010.
26
The results are available from the authors upon request.
26
The results show a negative relationship between size and credit risk, which is consistent
with possible diversification and scale economies benefits. Loan growth is associated with lower
credit risk in the following year as also identified by Clair (1992). We also find that higher
domestic interest rates have a positive influence on credit risk (loans are more difficult to repay if
rates are higher).
Islamic banks may have lower credit risk compared to conventional banks due to the
religiosity of clients that enhances loyalty and mitigates default and/or due to their special
relationship with their depositors. To investigate the former we include an interaction term for the
Islamic bank dummy and Muslim share in population (Islamic_Bank × Muslim_Share) reported
in column (7). The result shows that there is a negative relationship between the credit risk of
Islamic banks and the share of Muslims in the population. We find similar results (reported in
column (8)) when we use Legal_System in lieu of Muslim_Share as the religiosity proxy. In
column (9) the model now includes the Islamic bank/domestic interest rate interaction term
(Islamic_Bank × Domestic_Interest_Rate) and here we find that the credit risk of Islamic banks
is not significantly sensitive to domestic interest rates, while a one percent increase in domestic
rates (on average) is associated with 0.232 percent increase in the Loan_Loss_Reserve of
conventional banks. To analyze whether the relationship between Islamic banks and their
depositors can explain the higher loan quality of Islamic banks we include the interaction term of
the Islamic bank dummy and capital to asset ratio (Islamic_Bank × Capital_Asset_Ratio) and
report the estimation in column (10). The result shows that higher leverage is associated with
lower credit risk for Islamic compared to conventional banks.
We find a negative relationship between leverage and credit risk and also see that size and
leverage are linked in a similar manner. As we expect, there is a negative relationship between
bank assets size and clients’ religiosity (larger Islamic banks may move toward bigger clients less
27
sensitive to religious concerns). Moreover, Čihák and Hesse (2010) attribute the negative size
effect on Islamic bank stability to risk management limitations. In order to investigate this issue
further we include an interacted Islamic bank dummy with bank asset size (Islamic_Bank × Size)
and report findings in column (11). The result shows that size has a negative impact on credit risk
of Islamic banks, although the coefficient is significant only at the 10% level (possibly due to the
negative relationship between size and leverage).
[TABLE III]
4.2. INSOLVENCY RISK
Table IV reports the insolvency risk Equation again estimated using random effects. In
columns (1) to (6), we regress the insolvency risk proxy27 on our Islamic bank and Islamic
window bank dummy variables (Islamic_Bank & Islamic_Window_Bank), while adding different
classes of control variables in each step. Overall, we find no significant difference between
Islamic and conventional banks in terms of insolvency risk28. The results also show that higher
levels of non-interest income, cost inefficiency, share of Muslims in population, domestic interest
rates and GDP per capita are associated with lower bank stability. In terms of ownership
structure, we find that subsidiaries are less stable than domestic privately-owned banks. The
results also show that young banks are less stable than their more mature counterparts.
In columns 7 and 8 we report results where we replace the Zscore by the logarithm of its
first and second components and find no significant difference between Islamic and conventional
27
We use the logarithm of Zscore “Zscore” as the insolvency risk proxy; we find similar results when we employ the
absolute value of Zscore in our analysis, except that in one specification (when we control for all other factors)
Islamic banks exhibit higher stability than conventional banks at the 10% significance level, which is due to their
higher capitalization. Results are not presented here; they are available from the authors upon request.
28
We find similar results when we use clustered standard errors, assuming within country correlation of standard
errors. The results are available from the authors upon request.
28
banks. To investigate the possible impact of the religiosity of Islamic banks’ clients on stability,
in column (9), we add the interaction terms of Islamic bank dummy variable and Muslim share in
population (Islamic_Bank × Muslim_Share). The result shows no significant difference between
Islamic and conventional banks29. In columns (10), the interaction term of Islamic bank dummy
variable and interest rate (Islamic_Bank × Domestic_Interest_Rate) is included and we find no
significant difference between Islamic and conventional banks in terms of sensitivity to interest
rate changes. In column (11), we add the interaction term of Islamic bank dummy variable and
size (Islamic_Bank × Size), to investigate the size effect on insolvency risk of Islamic banks. The
result shows no significant difference between Islamic and conventional banks and again this is
supported in column (12), where we include the Islamic bank dummy and share of loans in total
earning assets interaction variable (Islamic_Bank × Loan_Total_Earning_Asset_Ratio). The
composition of total earning assets does not appear to have a significantly different impact on
Islamic banks’ stability compared to conventional banks.
[TABLE IV]
4.3. BANK INTEREST RATES
Table V illustrates estimates of Equation (3) using random effects. Column (1) shows net
interest margin (Net_Interest_Margin) regressed on the Islamic bank and Islamic window dummy
variables (Islamic_Bank & Islamic_Window_Bank) and a range of controls that include various
financial variables, ownership structure, age dummies, macroeconomic indicators, year and
country dummies. The result shows no significant difference between Islamic and conventional
banks. In columns (2) to (5), we replace net interest margin with the implicit interest income rate
29
We get similar results when we use Legal_System variable in lieu of Muslim_Share as the religiosity proxy, which
is not reported here, but is available upon request.
29
(Interest_Income_Rate), implicit interest expense rate (Interest_Expense_Rate), implicit interest
rate on loans (Loan_Rate) and implicit interest rate on deposits (Deposit_Rate) respectively.
Overall, we find little evidence that Islamic banks charge any special rent to their clients for
offering Shariá-compliant products30. We also find a positive impact of domestic interest rates on
net interest margins, implicit interest income and expense rates as well as on implicit interest rate
on deposits.
In columns (6) to (10), we include the interaction term of domestic interest rate and the
Islamic bank dummy (Islamic_Bank × Domestic_Interest_Rate) to investigate the sensitivity of
Islamic banks’ earnings and expenses to domestic interest rates compared to conventional banks.
The results show no significant difference between Islamic and conventional banks in terms of
the sensitivity of net interest margin, implicit interest rate on loans and implicit interest rate on
deposits to domestic interest rates. We do find, however, that implicit interest income and
implicit interest expense rates of Islamic banks are less sensitive to domestic interest rate levels
than for conventional counterparts.
[TABLE V]
4.4. ROBUSTNESS CHECKS AND FURTHER ISSUES
4.4.1 Credit Risk
In order to confirm our findings, we undertake a number of robustness checks. We find
that Islamic banks operating in countries with greater shares of Muslims in the population are less
exposed to credit risk than conventional banks. For further analysis, we re-estimate our credit risk
model for country sub-samples that have more than 90% Muslim populations (Muslim+90) and
30
As a robustness check, we assume within country correlation of standard errors, using clustered standard errors,
and find similar results. The results are available from authors upon request.
30
those with smaller populations (Muslim-90) shown in Annex 5 columns (1) and (2)
respectively31. The results show that Islamic banks are less exposed to credit risk only in
Muslim+90 countries, possibly because the clients of Islamic banks in those countries are, on
average, more concerned about their religious beliefs and hence are more risk averse than
conventional banks’ clients. We also find that the domestic interest rate coefficient is significant
only in the Muslim-90 sub-sample (where the share of domestic credit provided by the banking
system in GDP is 68% compared to 39% in Muslim+90 countries -implying sensitivity of loan
risk to interest rates in more leveraged economies).
In Annex 5 columns (3) and (4), the interaction term of Islamic bank dummy variable and
domestic interest rate (Islamic_Bank × Domestic_Interest_Rate) is added to the Muslim+90 and
Muslim-90 sub-samples respectively. Using the Muslim-90 sub-sample, we find that credit risk
of Islamic banks is less sensitive to interest rates compared to conventional banks. The results
imply that loan takers from Islamic banks have, on average, lower income gearing (the ratio of
interest payment to disposable income) so that they have lower sensitivity to interest rate
changes32. For the countries classified as the Muslim+90, no significant difference between
Islamic and conventional banks in terms of interest rate sensitivity is observed.
We also observe a positive relationship between leverage and loan quality of Islamic
banks. To disentangle the impact of greater market discipline associated with higher leverage,
from clients’ religiosity and investigate whether they cancel out each other, (as the religious
beliefs of clients may induce greater loyalty and thus reduce deposit withdrawal risk) we split our
31
The sample is sorted based on the Muslim population. Observations above the median are those with at least 90%
Muslims in their population (Muslim+90) and the remainder is placed in another category (“Muslim-90”). Countries
in the Muslim+90 category generally have legal systems primarily based on Sharia law, they have lower GDP per
capita and growth rates, but higher domestic interest rates compared to countries in the Muslim-90. Table B of
Annex 3 presents the macroeconomic and banking indicators for these two groups of countries.
32
Higher risk aversion of more religious individuals and possibly limited access to the credit market due to religious
restrictions can explain lower income gearing of loan takers of Islamic banks.
31
sample in the two groups of countries into high and low leveraged banks33. The estimates are
given in columns (5) to (8) in Annex 5. Interestingly, highly leveraged Islamic banks have less
risky loans even in the Muslim-90 countries. The Islamic bank dummy coefficient is larger (in
absolute value) for the Muslim+90 sub-sample. The Islamic bank dummy for lowly leveraged
banks in the Muslim+90 countries is significantly negative only at the 10% level. These results
suggest that although Islamic banks try to lower withdrawal risk of investment account depositors
by paying market returns, leverage seems to discipline Islamic banks more effectively than their
conventional counterparts.
The previously reported results also show a negative impact of size on the loan quality for
Islamic banks. To further analyze size effects taking into account the impact of leverage, in
columns (9) to (12), we estimate our model using the following four sub-samples: small and
highly leveraged banks, small and lowly leveraged banks, large and highly leveraged banks, large
and lowly leveraged banks34. The results show no significant difference between low leveraged
Islamic banks and their conventional counterparts, irrespective of whether they are classified as
large or small banks. For more highly leveraged banks, we find that credit risk of small and high
leveraged bank are significantly lower than their conventional counterparts. For large and high
leveraged banks, the coefficient on the Islamic bank dummy is significantly negative only at the
33
We classify banks as high or low leveraged, based on the median value of Capital_Asset_Ratio in each of the two
groups of countries.
34
Banks with total assets less than one billion US$ are classified as small. De Young, et al. (2004) claim that small
and large banks operate differently - small banks generally deal with small companies, which are relatively opaque.
Large banks, however, can benefit from economies of scale, standardized products and are more transaction (as
opposed to relationship) based. They mostly analyze hard information obtained from transparent firms. Hence,
empirical investigation of the sub-samples might show the possible impact of different customer relationships on the
credit risk of Islamic versus conventional banks.
32
10% level, which implies an inverse relationship between size and the credit risk of Islamic
banks35.
To investigate whether the credit risk feature of Islamic banks differs during the recent
financial crisis, we estimate the model, using the two sub-periods: the pre-crisis period, i.e. 20032007, and the crisis period, i.e. 2008-200936. The estimations are presented in columns (13) and
(14) of Annex 5. In both periods, Islamic banks exhibit lower credit risk than conventional banks.
4.4.2. Insolvency Risk
We find little evidence that Islamic banks’ stability is affected differently from
conventional banks by the share of Muslim in population. For further investigation, we estimate
the insolvency risk model using the sub-samples of Muslim+90 and Muslim-90 and report the
results in columns (1) and (2) in Annex 6. We observe no significant difference between Islamic
and conventional banks in any of the two sub-samples. The estimations also suggest a positive
relationship between interest rate and insolvency risk only for Muslim+90 countries, possibly
because domestic interest rates in these countries are, on average, higher than the other groups of
countries.
In columns (3) and (4) of Annex 6, the Islamic bank dummy and domestic interest rate
interaction term (Islamic_Bank × Domestic_Interest_Rate) is included in the model, using the
Muslim+90 and Muslim-90 sub-samples. The results show no significant difference between
Islamic and conventional banks in terms of sensitivity to interest rate changes.
35
We use Impaired_Loans and Loan_Loss_Provision as the credit risk proxy in lieu of Loan_Loss_Reserve and find
almost similar results.
36
BIS (2010) identifies the pre-crisis period from January 2003 to June 2007 and the acute-crisis as July 2007 to
March 2009. Since quarterly data are not available, we consider 2003-2007 and 2008-2009 as the pre-crisis and the
crisis periods respectively.
33
In order to compare the stability of small and large Islamic and conventional banks, we
split the sample into small and large banks. Column (5) presents the estimations using the small
banks sub-sample. The results show that small Islamic banks are more stable than similar sized
conventional banks. The absolute value of the Zscore is on average, 1.47 (1.47 = ' .>>) higher
for small Islamic banks than for similar-sized conventional banks, (or to put another way, is
4.67% (4.67 = 100 ×
.B=
C D.EF
) higher than the average Zscore of small conventional banks.) In
columns (6) and (7), we replace the insolvency risk proxy with the logarithm of its first and
second components respectively and find that the stability of small Islamic banks is due to their
higher capitalization. In columns (8) to (10), we estimate the model using the large banks subsample, including the stability proxy and the logarithm of its first and second components. The
estimations show no significant difference between large Islamic and conventional banks.
Column (11) presents the estimation for the pre-crisis period (2003-2007) and also shows
no significant difference between Islamic and conventional banks. In column (12) we use the
crisis period (2008-2009) sub-sample and find that Islamic banks are less stable than
conventional banks37. In columns (13) and (14), we estimate the model for small and large bank
sub-samples during the crisis period. The results show that only large Islamic banks are less
stable than similar sized conventional banks, while no significant difference is observed between
small banks.
4.4.3. Bank (Implicit) Interest Rates
In order to investigate whether our bank interest rate findings are robust across different
specifications, we re-estimate the models reported in Table V, using the Muslim+90 / Muslim-90
37
For the crisis period, we consider the Zscore calculated from 2006-2008 and 2007-2009 windows. We also
estimate the model using the Zscore calculated based on 2007-2009 window and find qualitatively similar results.
34
sub-samples and small / large banks sub-samples. The results (not reported here) support our
previous finding that Islamic banks charge no special rent to their clients for offering Shariácompliant products. We also find that the positive relationship between domestic interest rates
and net interest margin, implicit interest income and expense rates, and implicit interest rate on
deposits holds for the Muslim+90 and large banks sub-samples. Our results also show that the
lower sensitivity of implicit interest income and expense rates of Islamic banks compared to
those of their conventional counterparts are in line with previous results, when we use the
Muslim+90 and large bank sub-samples.
We also investigate these relationships before and during the recent financial crisis. The
results are presented in Annex 7. In columns (1) to (5), we estimate the model for the pre-crisis
period (2003-2007) and the estimations on the crisis period (2008-2009) are illustrated in
columns (6) to (10). For the pre-crisis period, we find no significant difference between Islamic
and conventional banks, except for the implicit interest rate on deposits, wherein Islamic banks
exhibit lower sensitivity to interest rate changes than conventional banks.
In the crisis period, the results are different. We find higher sensitivity of Islamic banks’
net interest margin to interest rate movements than for conventional banks. Columns (7) and (8)
can explain this result. While the implicit interest income rate of Islamic banks is less sensitive to
interest rate changes than for conventional banks, no significant difference is found for the
implicit interest expense rate. Finally, both implicit interest rates on loans and deposits of Islamic
banks exhibit lower sensitivity to interest rate changes, than those of conventional banks.
4.4.4. Other Checks
As further robustness checks we exclude banking systems that are entirely Islamic - Iran
and Sudan - from the sample and re-estimate models (1) to (3), the results remain significantly
35
unchanged38. Turkey experienced particularly high levels of domestic interest rates especially at
the beginning of the previous decade and this may have influenced our interest rate findings. We
therefore estimate our three models excluding information on Turkey and re-examine the
sensitivity of Islamic banks to interest rates. The results are mainly in-line with our previous
findings39. However, here we do find that, for our Muslim+90 sub-sample, insolvency risk is
higher for conventional banks at the five percent significance level. The absolute value of Zscore
is on average 1.52 (1.52 = ' .B ) higher for Islamic than conventional banks, which is
equivalent to 4.84% (4.84 = 100 ×
.J
C D.EEK
) of the average Zscore of conventional banks operating
in Muslim+90 countries. However, contrary to our previous findings, no significant sensitivity of
insolvency risk and net interest margin to domestic interest rates is observed. As a final
robustness check, we estimate the model using the logarithm of the Zscore where return volatility
is calculated over the whole period (for banks with at least four consecutive observations). On the
right hand side of the Equation, we use the mean value of the explanatory variables over the
sample period. This approach provides us with between group estimation and reduces noise
although we have to use a cross-sectional (rather than panel) estimation approach. Similar to our
previous findings, smaller Islamic banks exhibit lower insolvency risk than similar-sized
conventional banks and we find no difference between larger banks.
38
In response to the referee’s comment we investigate whether the performance of conventional and Islamic banks
are linked to the market share of Islamic banking in countries with dual banking systems. To do this we include
Islamic bank assets market share (per country per year) into our three models and re-estimate our models using subsamples for Islamic and conventional banks. The results show that higher Islamic banks’ assets market share is
associated with more stable conventional banks (at the 1% significance level) but less stability for Islamic banks (at
the 10% level). This latter outcome is driven by the capital variable in the Zscore. We also find a negative correlation
between the assets market share of Islamic banks and their Interest_Expense_Rate at the 5% significance level.
These results are available from the authors on request.
39
Results are available from the authors upon request.
36
5. Summary and Conclusion
This paper analyzes the risk and stability features of Islamic banks. The obligations of
Islamic banks towards depositors (investment account holders) are different from those of
conventional banks and hence they face different risks. Conventional banks have to fulfill their
obligations towards depositors irrespective of their profits or losses whereas Islamic banks are
supposed to share the realized profit or loss with investment account holders. This special
relationship may discipline Islamic banks more effectively by imposing higher withdrawal risk.
In practice, to avoid withdrawal risk, Islamic banks tend to partly deviate from the PLS principles
of Islamic finance. They pay a relatively competitive rate of return to investment account holders,
regardless of their realized performance. On the asset side, it appears that Islamic banks mainly
apply non-PLS modes of Islamic finance which are in nature closer to conventional finance.
Nevertheless, Islamic banks still may face extra risks because of the complexity of Islamic modes
of finance and limitations in their funding, investment and risk management activities. On the
other hand, customers of Islamic banks are expected to be more concerned about their religious
beliefs. Taking into account the positive relationship between religiosity and an individual’s risk
aversion, Islamic banks may face less risk (credit risk) than conventional banks.
We attempt to investigate the credit risk and stability features of Islamic commercial
banks using a sample of 553 conventional and Islamic banks from 24 countries between 1999 and
2009. This research also explores whether Islamic banks charge extra cost to their clients for
offering Shariá compliant financial products. After controlling for various factors we find that
Islamic banks have lower credit risk than conventional banks, and this is specifically the case for
small highly leveraged banks, or operating in predominantly Muslim countries (those where
Muslims exceed 90% Muslims of the population). In terms of insolvency risk, small Islamic
banks also appear to exhibit greater stability than conventional banks, as they are more
37
capitalized; however, no significant difference between large Islamic and conventional banks is
observed. Loan quality, (implicit) interest income and (implicit) interest expense of Islamic banks
are less sensitive to domestic interest rates compared to conventional counterparts; however, the
sensitivity of Islamic banks’ solvency position to interest rates is not significantly different from
that of their conventional counterparts. Finally, we find little evidence that Islamic banks charge
rents to their customers for offering Shariá compliant financial products. The fact that Islamic
banks do not appear to emulate the risk and stability characteristics of their conventional
counterparts has implications for policymakers (in terms of whether there should be a different
legislation for the two types of banks), regulators (should they be regulated differently) and
market participants (can traditional risk management tools be used to gauge and control these
risks?)
38
Annex 1. Variable Description
This annex describes the variables used in this study.
Credit Risk Proxies
Description
Loan_Loss_Reserve
The ratio of loan loss reserves to gross loans. Loan loss reserve is considered for the whole loans portfolio, and not only for impaired loans. The
managers assess the quality of the loans portfolio and determine the required reserves. Then the current level of Loan loss reserve will be adjusted to
reach the required level. The adjustment will be reflected in the loan loss provision stipulated in the income statement. When a bank decides to write
off a loan, the loan amount would be deducted from the Loan loss reserve.
Impaired_Loans
The ratio of impaired loans to gross loans. Impaired loans increase when a bank classifies a specific loan or a part of a loan portfolio as bad. It
decreases when either a bank re-assesses a problem loan or part of a portfolio or when a bank writes off a loan or a part of loan portfolio.
Loan_Loss_Provision
The ratio of loan loss provision to average gross loans. Loan loss provision is the incurred cost to banks of adjusting the loan loss reserve or writing off
a loan. Hence, Loan_Loss_Reserve and Impaired_Loans are stocks while Loan_Loss_Provision is a flow and is stipulated in the income statement. It is
possible to have a negative loan loss provision in one period, when the required loan loss reserve is lower than the current reserve.
Insolvency Risk Proxies
Zscore_rw
Logarithm of rolling-window Zscore which is equal to (ROAA+CAR)/SDROAA_rw, SDROAA_rw = Standard deviation of ROAA over 3 years
(current year and two previous consecutive years). Banks need to have three consecutive observations. Acquiring banks are excluded from the sample,
since the volatility on their assets returns can be due to the acquisition.
Zscore_P1_rw
Logarithm of ROAA/SDROAA3_rw.
Zscore_P2_rw
Logarithm of Capital_Asset_Ratio/SDROAA3_rw.
Zscore
Logarithm of (M_ROAA+M_Capital_Asset_Ratio)/SDROAA, M_ROAA = Mean of ROAA over the sample period, M_Capital_Asset_Ratio =Mean
of Capital_Asset_Ratio over the sample period, SDROAA= standard deviation of ROAA over the sample period (banks needs to have at least four
consecutive observations).
Zscore_P1
Logarithm of M_ROAA/SDROAA.
Zscore_P2
Logarithm of M_ETA/SDROAA.
Bank Interest Rate Proxies
Net_Interest_Margin
(Interest Income – Interest Expense) / Average Earning assets.
Interest_Income_Rate
Interest income divided by average earning assets for conventional banks and mark-up income over average earning assets for Islamic banks.
Interest_Expense_Rate
Interest expense divided by average interest bearing liabilities and profit payouts over average profit bearing liabilities for Islamic banks.
Loan_Rate
Interest income on loans divided by average gross lending for conventional banks and mark-up income on lending divided by average gross loans for
Islamic banks.
Deposit_Rate
Interest expense on customer deposit divided by average customer deposits for conventional banks and profit payouts on customer deposits divided by
average customer deposits for Islamic banks.
Financial Ratio
Size
Logarithm of total assets.
Market_Share
Logarithm of market share of total assets.
Capital_Asset_Ratio
Equity capital to asset ratio.
ROAA
Return on average assets.
ROAE
Return on average equity.
Loan_Total_Earning_Asset_Ratio
Share of net loans in total earning assets.
Loan_Growth
Annual growth rate of gross loans.
Asset_Growth
Annual growth rate of total assets.
Noninterest_Income
Share of non-interest income in total operating income.
Cost_Inefficiency
Cost to income ratio.
Ownership Structure
State_Bank
State-owned bank dummy that takes the value of one if the bank is state-owned, and zero otherwise.
Foreign_Bank
Foreign-owned bank dummy that takes the value of one if the bank is Foreign-owned, and zero otherwise.
Subsidiary
Subsidiary dummy that takes the value of one if the bank is subsidiary, and zero otherwise.
Banks Age or Experience Level
Young_ Bank
Young bank dummy that takes the value of one, if the bank has been operating for at most three years, and zero otherwise.
Middle-Aged_Bank
Middle-aged bank dummy that takes the value of one if the bank has operated from three to seven years, and zero otherwise.
Country Level Variables
Muslim_Share
Share of the Muslim population in the total population of each country.
Legal_System
Takes the value of zero, if the country does not use Shariá law to define its legal system, the value one for countries which consider Shariá together
with other legal systems, and has the value two if the legal system is based exclusively on Shariá law.
Domestic_Interest_Rate
Deposit Interest Rate provided by the World Bank website; for years and countries with missing observations, the data is obtained from the central
bank web-sites.
HHI
Hirschman-Herfindahl index (HHI) is a proxy for market concentration: LLM,N = ∑VUOP%QRS_T##'Q#U,N, ⁄∑VU P%QRS_T##'Q#U,N, X . It has a value
between zero and one. Higher values show that the market is more concentrated.
GDP_Per_Capita
GDP per capita in US$.
GDP_Per_Capita_Growth
Annual growth rate of GDP per capita.
39
Annex 2.
Table A. Islamic, Conventional and Islamic Window Banks Distributions across Countries
This table presents the number of Islamic, conventional and Islamic window banks across 24 countries, over the
1999-2009 period.
Islamic bank
Islamic Window Bank
Conventional Bank
Total
Country
Banks
Observations
Banks
Observations
Banks
Observations
Banks
Observations
101
Algeria
3
19
1
9
11
73
15
Bahrain
6
44
6
47
1
8
13
99
Bangladesh
5
42
9
71
19
175
33
288
Brunei
4
19
0
0
1
8
5
27
Egypt
3
20
6
57
25
183
34
260
Gambia
1
4
0
0
7
38
8
42
Indonesia
2
21
12
73
70
472
84
566
Iran
12
95
0
0
0
0
12
95
Iraq
4
13
0
0
8
42
12
55
Jordan
3
21
0
0
10
97
13
118
Kuwait
3
21
1
5
5
46
9
72
Lebanon
1
7
3
23
49
334
53
364
Malaysia
17
92
12
104
24
123
53
319
Mauritania
1
9
3
21
5
36
9
66
Pakistan
6
26
12
105
14
84
32
215
Qatar
4
34
3
17
3
34
10
85
Saudi Arabia
3
28
7
72
0
0
10
100
Senegal
1
6
0
0
12
88
13
94
Sudan
20
135
0
0
2
7
22
142
Syria
2
5
0
0
11
61
13
66
Tunisia
1
10
1
9
13
90
15
109
Turkey
4
15
0
0
42
246
46
261
UAE
8
62
5
27
16
153
29
242
Yemen
4
34
0
0
6
50
10
84
118
782
81
640
354
2448
553
3870
Total
Table B. Ownership Structure and Age of Banks
This table presents the ownership structure and age (experience level) of Islamic, conventional and Islamic
window banks
Islamic bank
Islamic Window Bank
Conventional Bank
Total
Banks
Observations
Banks
Observations
Banks
Observations
Banks
Observations
State-owned Banks
16
125
8
59
38
316
62
500
Foreign-owned Banks
26
165
5
39
32
198
63
402
Subsidiaries
14
73
15
87
99
624
128
784
Private-owned Banks
62
419
53
455
185
1310
300
2184
3870
Total
118
782
81
640
354
2448
553
Young Banks
47
115
11
28
51
118
109
261
Middle-Aged Banks
13
142
9
40
37
220
59
402
Matured Banks
58
525
61
572
266
2110
385
3207
Total
118
782
81
640
354
2448
553
3870
State-owned banks: state ownership > 50%. Foreign-owned banks: foreign ownership > 50%. Subsidiaries: parent ownership =
100%. Private-owned banks: domestic private ownership > 50%. Young banks: operating less than 3 years. Middle aged banks:
operating between 3 to 7 years. Matured banks: operating more than 7 years. The information is obtained from Bankscope
database and web-sites of banks.
40
Annex 3.
Table A. Macroeconomic and Banking Indicators across Countries
This table presents the mean value of macroeconomic and banking indicators across 24 countries, over the 1999-2009 period.
Countries
Muslim_Share
(%)
Legal_System
Domestic_Interest_Rate
(%)
HHI
GDP_Per_Capita
($)
GDP_Per_Capita_Growth
(%)
Domestic_Credit
(%)
Explicit_Deposit_Insurance
Algeria
98
1
4.1
0.26
6,796
2.1
17
1
Bahrain
81
1
3.0
0.26
27,275
3.7
51
1
Bangladesh
89.6
0
8.5
0.16
1,047
4.0
51
1
Brunei
67.2
1
1.9
0.64
47,490
-0.5
26
0
Egypt
94.6
1
7.8
0.18
4,383
3.3
92
0
95
1
14.5
0.41
1,156
1.0
25
0
Gambia
Indonesia
88.2
0
12.0
0.11
3,152
3.4
48
1
Iran
99.4
2
12.6
0.30
9,024
3.3
37
0
0
Iraq
99
1
7.5
0.57
3,396
0.3
0
Jordan
98.2
1
5.0
0.51
4,227
3.5
94
1
Kuwait
95
1
4.2
0.38
39,922
2.3
74
0
Lebanon
59.3
0
9.2
0.17
9,558
2.9
176
1
Malaysia
60.4
0
3.2
0.10
11,393
2.9
132
1
Mauritania
99.1
1
8.2
0.36
1,679
1.4
-3
0
Pakistan
96.3
1
4.8
0.17
2,114
2.2
44
0
Qatar
77.5
1
3.6
0.38
67,840
3.5
53
0
97
2
4.0
0.26
20,451
0.7
28
0
Saudi Arabia
Senegal
96
0
3.5
0.19
1,558
1.5
23
0
Sudan
71.3
1
13.1
0.18
1,633
3.9
13
1
Syria
92.2
0
6.2
0.46
3,974
1.2
33
0
Tunisia
99.5
1
3.4
0.30
6,309
3.7
72
0
Turkey
98
0
38.0
0.11
10,332
1.7
47
1
UAE
76.2
1
3.3
0.14
47,863
1.3
62
0
Yemen
99.1
1
13.4
0.19
2,148
0.9
9
0
Muslim_Share = Share of the Muslim population in the total population of each country, Legal_System = Takes the value of zero, if the country does not use Shariá law to
define its legal system, the value one for countries which consider Shariá together with other legal systems, and has the value two if the legal system is based exclusively
on Shariá law, Domestic_Interest_Rate = Deposit Interest Rate provided by the World Bank website, HHI = Hirschman-Herfindahl index which is a proxy for market
concentration, GDP_Per_Capita = GDP per capita in US$, GDP_Per_Capita_Growth = Annual growth rate of GDP per capita, Domestic_Credit = Domestic credit
provided by banking system as the percentage of GDP, Explicit_Deposit_Insurance = Explicit Deposit Insurance Scheme Dummy, that takes the value of one for countries
with explicit deposit insurance scheme and takes zero otherwise.
Table B. Macroeconomic and Banking Indicators across Two Groups of Countries
This table presents the mean value of macroeconomic and banking system indicators across two groups of countries (Muslim+90 & Muslim-90), over
the 1999-2009 period.
Domestic_Interest_Rate (%)
HHI
GDP_Per_Capita ($)
GDP_Per_Capita_Growth (%)
Domestic_Credit (%)
Muslim+90
9.1
0.31
7,831
1.9
39
Muslim-90
6.4
0.24
24,139
2.8
68
Muslim+90 = Covers the countries with at least 90% of Muslims in their population, Muslim-90 = Covers the countries with less than 90% of Muslims in their population.
Domestic_Interest_Rate = Deposit Interest Rate provided by the World Bank website, HHI = Hirschman-Herfindahl index which is a proxy for market concentration,
GDP_Per_Capita = GDP per capita in US$, GDP_Per_Capita_Growth = Annual growth rate of GDP per capita, Domestic_Credit = Domestic credit provided by banking
system as the percentage of GDP.
41
Annex 4. Correlation Matrix
This table presents the pair-wise correlation between the variables used in our analysis.
1
(1) Loan_Loss_ Reserve
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1.00
(2) Impaired_Loans
0.79
1.00
(3) Loan_ Loss_Provision
0.26
0.26
1.00
(4) Zscore_rw
-0.08
-0.13
-0.07
1.00
(5) Net_Interest_Margin
-0.09
-0.13
0.06
-0.08
1.00
(6) Interest_Income_Rate
-0.01
-0.01
0.02
-0.13
0.60
1.00
(7) Interest_Expense_Rate
0.01
0.05
-0.01
-0.12
0.09
0.73
1.00
(8) Loan_Rate
-0.05
-0.13
0.01
-0.08
0.55
0.80
0.65
1.00
(9) Deposit_Rate
-0.02
-0.05
0.01
-0.12
0.09
0.70
0.89
0.65
1.00
(10) Islamic_Bank
-0.08
-0.08
-0.04
-0.03
0.02
-0.12
-0.12
-0.01
0.04
1.00
(11) Islamic_Window_Bank
-0.02
-0.01
0.00
0.03
-0.09
-0.11
-0.10
-0.09
-0.07
-0.22
1.00
(12) Size
-0.17
-0.21
-0.08
0.01
-0.12
-0.09
-0.02
-0.02
0.10
-0.04
0.12
(13) Market_Share
-0.10
-0.13
0.01
0.00
-0.07
-0.16
-0.16
-0.09
-0.04
0.04
0.07
1.00
0.72
1.00
(14) Capital_Asset_Ratio
0.05
0.01
0.00
0.17
0.14
-0.03
-0.12
0.00
-0.10
0.13
-0.08
-0.34
-0.37
1.00
(15) Loan_Total_Earning_Asset_Ratio
-0.30
-0.23
-0.02
0.07
-0.01
-0.06
-0.09
0.10
0.11
0.06
0.04
0.08
0.06
-0.09
1.00
(16) Loan_Growth
-0.27
-0.33
-0.03
-0.01
0.11
0.05
0.00
0.06
0.00
0.08
-0.03
-0.06
-0.04
0.00
0.17
1.00
(17) Asset_Growth
-0.20
-0.24
-0.03
-0.01
0.05
0.05
0.03
0.04
0.04
0.10
-0.03
-0.05
-0.06
-0.01
0.04
0.60
1.00
(18) Noninterest_Income
0.05
0.06
0.05
-0.07
-0.42
-0.28
-0.09
-0.14
-0.04
0.14
-0.04
-0.06
0.10
-0.01
0.06
-0.01
-0.02
1.00
(19) Cost_Inefficiency
0.05
0.17
-0.02
-0.15
-0.10
0.10
0.21
0.13
0.18
0.06
-0.12
-0.11
-0.06
-0.09
-0.01
0.01
0.00
0.09
1.00
(20) State_Bank
0.01
-0.03
0.00
0.01
-0.08
-0.05
0.00
-0.05
0.00
0.06
-0.04
0.11
0.16
-0.07
0.04
-0.01
-0.02
0.08
-0.02
1.00
(21) Foreign_Bank
0.05
0.06
0.02
-0.02
-0.05
-0.06
-0.03
-0.10
-0.02
0.15
-0.05
-0.11
-0.09
0.03
-0.02
-0.01
0.01
0.06
0.06
-0.13
1.00
(22) Subsidiary
-0.01
0.01
-0.02
-0.06
0.08
-0.04
-0.13
0.03
-0.14
-0.14
-0.05
-0.03
-0.07
0.04
-0.03
-0.05
-0.05
0.00
0.01
-0.19
-0.18
(23) Young_ Bank
-0.03
-0.03
-0.02
-0.07
0.02
-0.03
-0.04
0.05
-0.05
0.16
-0.03
-0.17
-0.09
0.13
-0.07
0.15
0.15
0.05
0.09
-0.08
-0.03
1.00
0.06
1.00
(24) Middle_Aged_Bank
0.01
-0.01
0.02
-0.04
0.04
0.03
0.01
0.08
-0.02
0.12
-0.04
-0.08
-0.02
0.04
-0.04
-0.01
0.02
0.04
0.01
-0.06
-0.01
0.05
-0.10
1.00
(25) Muslim_Share
-0.03
-0.06
0.04
-0.16
0.14
0.12
0.13
0.31
0.21
-0.06
-0.01
0.00
0.13
0.01
0.09
0.05
0.01
0.09
0.04
0.10
0.04
-0.03
0.06
0.04
1.00
(26) Legal_System
0.05
-0.02
0.05
-0.01
-0.07
-0.30
-0.30
-0.19
-0.13
0.34
0.10
0.05
0.26
0.09
0.03
0.03
0.03
0.15
-0.10
0.20
0.06
-0.12
0.08
0.02
0.38
1.00
(27) Domestic_Interest_Rate
0.00
0.02
0.06
-0.23
0.37
0.70
0.66
0.58
0.67
-0.09
-0.16
-0.01
-0.08
-0.02
-0.13
-0.01
-0.02
-0.15
0.19
-0.02
-0.03
-0.01
-0.07
-0.03
0.22
-0.26
1.00
(28) HHI
0.01
0.01
0.03
0.02
-0.04
-0.21
-0.24
-0.24
-0.20
0.14
-0.05
-0.05
0.23
0.10
0.03
0.02
0.04
0.10
-0.10
0.08
0.00
-0.10
0.11
0.02
0.25
0.44
-0.18
(29) GDP_Per_Capita
-0.04
-0.08
-0.03
0.10
-0.08
-0.26
-0.25
-0.25
-0.20
0.13
0.05
0.15
0.09
0.15
0.01
-0.01
0.00
-0.11
-0.24
0.07
-0.13
-0.10
0.02
-0.03
-0.25
0.28
-0.18
0.18
1.00
(30) GDP_Per_Capita_Growth
-0.10
-0.12
-0.03
0.02
-0.05
-0.07
-0.10
0.02
0.13
0.03
0.01
0.00
-0.07
0.04
0.10
0.11
0.10
0.09
-0.02
-0.04
0.01
-0.01
0.03
0.00
-0.02
-0.04
-0.19
-0.05
-0.04
42
1.00
1.00
Annex 5. Credit Risk Model
This table presents the estimation of credit risk model, using different sub-samples: Muslim+90/Muslim-90, low/high leveraged banks, small/large banks and pre-crisis/the crisis periods.
(1)
(2)
(3)
(4)
Muslim+90
Muslim-90
Muslim+90
Muslim-90
Variables
Islamic_Bank (α1)
(5)
(6)
Muslim+90
(7)
(8)
Muslim-90
(9)
(10)
Small Banks
(11)
(12)
Large Banks
Highly
Leveraged
Lowly
Leveraged
Highly
Leveraged
Lowly
Leveraged
Highly
Leveraged
Lowly
Leveraged
Highly
Leveraged
Lowly
Leveraged
(13)
(14)
Pre-Crisis
Period
Crisis
Period
-6.364***
(-3.44)
-1.649
(-0.98)
-1.080
(-0.83)
-0.719
(-0.72)
-4.658
(-1.38)
-1.469
(-0.86)
0.773
(0.47)
-0.590
(-0.59)
-10.112***
(-3.89)
-1.159
(-0.63)
-3.796*
(-1.67)
-3.340
(-1.24)
-2.947**
(-2.11)
-1.159
(-0.91)
-1.186
(-0.55)
-0.265
(-0.14)
-7.564**
(-2.52)
-0.543
(-0.32)
-2.698
(-0.91)
-0.673
(-0.26)
-2.137*
(-1.81)
-0.488
(-0.40)
-0.626
(-0.41)
-1.428
(-0.71)
-4.731***
(-3.82)
-1.147
(-0.95)
-4.077**
(-2.08)
-1.070
(-0.55)
-1.606***
(-2.82)
-0.285
(-0.79)
-1.608***
(-2.83)
-0.362
(-0.97)
-1.415
(-1.62)
-1.419
(-1.62)
-0.018
(-0.06)
-0.410
(-0.68)
-0.343
(-0.64)
-1.028**
(-2.50)
-0.227
(-0.56)
-0.325
(-0.91)
-0.495
(-1.56)
-0.849
(-0.88)
0.213
(0.29)
-0.081
(-1.02)
-0.496
(-0.61)
0.001
(0.03)
0.209
(0.29)
-0.082
(-1.04)
-0.190
(-0.22)
0.001
(0.02)
-0.319
(-0.15)
-0.062
(-0.38)
0.072
(0.09)
-0.088
(-1.61)
-0.602
(-1.09)
-0.050
(-1.39)
-1.485
(-1.21)
-0.083
(-1.17)
-0.834
(-0.60)
-0.446*
(-1.70)
-0.190
(-0.41)
-0.092***
(-3.04)
-1.450**
(-2.06)
-0.054
(-1.17)
0.163
(0.23)
-0.130
(-1.63)
-1.082**
(-2.02)
-0.046
(-1.01)
-1.009
(-0.29)
-0.051
(-0.84)
-0.032***
(-3.11)
-0.024***
(-3.92)
-0.032***
(-3.09)
-0.024***
(-3.91)
-0.035***
(-2.80)
-0.022*
(-1.92)
-0.017***
(-3.16)
-0.024***
(-3.58)
-0.027**
(-2.26)
-0.023***
(-4.40)
-0.018**
(-2.01)
-0.011
(-0.85)
-0.019**
(-2.31)
-0.009
(-1.07)
0.002
(0.12)
0.034**
(2.43)
-0.026
(-1.44)
-0.008
(-0.83)
0.003
(0.15)
0.034**
(2.44)
-0.026
(-1.40)
-0.006
(-0.59)
0.028
(0.86)
0.025*
(1.77)
0.001
(0.04)
0.023
(1.49)
-0.010
(-0.54)
0.013
(1.38)
-0.012
(-0.57)
-0.023*
(-1.78)
0.015
(0.71)
0.005
(0.38)
-0.020
(-1.03)
-0.030**
(-2.07)
0.015
(0.96)
0.023
(1.63)
-0.021
(-1.03)
0.001
(0.05)
0.007
(0.30)
-0.006
(-0.46)
-0.020
(-0.84)
-0.001
(-0.04)
State_Bank (α9)
2.450
(1.55)
-2.637**
(-2.35)
2.387
(1.50)
-2.687**
(-2.39)
3.637*
(1.83)
-0.096
(-0.05)
-1.263
(-1.29)
-2.933*
(-1.78)
0.501
(0.26)
-2.517
(-0.83)
0.274
(0.22)
-0.649
(-0.44)
-1.705
(-1.42)
-1.087
(-0.63)
Foreign_Bank (α 10)
3.009*
(1.69)
-1.844
(-1.31)
0.895
(0.57)
0.930
(0.83)
2.832
(1.55)
-1.775
(-1.26)
1.029
(0.66)
0.948
(0.85)
4.104**
(1.98)
0.355
(0.21)
2.099
(0.93)
-4.859***
(-3.03)
3.062
(1.17)
1.606
(1.07)
-0.419
(-0.14)
-0.734
(-0.46)
4.046**
(1.99)
4.709***
(2.77)
2.747
(0.89)
-1.729
(-0.82)
-1.673
(-1.23)
0.488
(0.39)
-0.783
(-0.49)
-1.763
(-1.59)
3.482**
(1.99)
-0.777
(-0.76)
0.135
(0.06)
-1.195
(-0.80)
Young_Bank (α12)
-0.728
(-0.30)
0.379
(0.17)
-0.765
(-0.31)
0.422
(0.19)
3.111
(0.74)
0.562
(0.20)
-0.786
(-0.79)
2.951
(0.71)
-2.567
(-1.32)
3.597
(1.02)
1.565
(0.59)
1.012
(0.50)
0.718
(0.42)
27.655***
(7.45)
Middle_Aged_Bank (α13)
1.213
(0.91)
-0.013
(-0.18)
-0.678
(-0.68)
0.311***
(2.66)
1.210
(0.92)
-0.008
(-0.10)
-0.771
(-0.76)
0.335***
(2.77)
-0.570
(-0.28)
0.148
(0.60)
3.097*
(1.82)
-0.043
(-0.45)
-1.351**
(-2.13)
0.165
(1.34)
-0.576
(-0.47)
0.178
(1.21)
-1.362
(-1.03)
0.271
(1.60)
0.992
(0.66)
0.175
(1.23)
-0.619
(-0.67)
0.257
(1.37)
-0.629
(-0.73)
-0.146*
(-1.71)
0.427
(0.32)
0.170
(1.31)
0.149
(0.13)
-0.133
(-0.35)
-0.209
(-0.59)
-0.355**
(-2.25)
Islamic_Window_Bank (α 2)
Size (α3)
Market_Share (α 4)
Capital_Asset_Ratio (α5)
Loan_Growth (α 6)
Noninterest_Income (α7)
Cost_Inefficiency (α 8)
Subsidiary (α 11)
Domestic_Interest_Rate (α15)
Islamic_Bank ×
Domestic_Interest_Rate (αID)
HHI (α16)
GDP_Per_Capita (α17)
GDP_Per_Capita_Growth(α 18)
Constant (α0)
Number of Obs
R-squared
H0: α 15 = αID = 0 (F-stat.)
H0: α 15 + α ID = 0 (F-stat.)
-13.046***
(-2.61)
4.679
(0.72)
-13.143***
(-2.62)
4.621
(0.71)
-10.477
(-0.95)
-5.938
(-1.19)
8.099
(0.80)
5.854
(0.79)
1.319
(0.18)
-3.810
(-0.93)
5.343
(0.82)
4.374
(0.79)
-1.571
(-0.43)
-8.379
(-0.52)
0.082
(0.28)
0.095
(1.31)
0.156
(1.44)
-0.193**
(-2.12)
0.086
(0.29)
0.092
(1.28)
0.166
(1.53)
-0.181**
(-2.03)
0.023
(0.04)
0.271
(1.34)
-0.271
(-0.59)
0.059
(0.65)
-0.051
(-0.25)
0.027
(0.26)
0.110
(0.76)
-0.338**
(-2.12)
-0.381
(-1.06)
0.010
(0.06)
0.070
(0.15)
0.058
(0.64)
0.095
(0.35)
0.024
(0.28)
0.174
(1.32)
-0.146
(-1.49)
0.240
(1.37)
0.000
(0.00)
-0.093
(-0.30)
0.001
(0.02)
29.786***
(4.71)
11.485*
(1.86)
29.928***
(4.73)
12.181*
(1.95)
29.423
(1.24)
0.000
(.)
8.172*
(1.75)
19.755*
(1.76)
19.677*
(1.84)
33.249***
(3.43)
6.286
(0.87)
16.851**
(2.44)
12.695**
(2.22)
29.487
(1.36)
798
0.254
1,099
0.150
798
0.257
1,099
0.150
397
0.359
401
0.252
544
0.245
556
0.132
463
0.433
468
0.174
478
0.193
489
0.169
863
0.192
428
0.219
0.35
0.34
9.25***
0.01
Dependent Variable: The ratio of loan loss reserves on gross loans is used as the credit risk proxy.
43
Explanatory Variables: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets, Market_Share = Logarithm of market share of total assets,
orthogonalized on Size, Capital_Asset_Ratio = The ratio of equity capital on total assets, Loan_Growth = Annual growth rate of gross loans, Noninterest_Income = Share of non-interest income in total operating income,
Cost_Inefficiency = Cost to income ratio, State_Bank = State-owned bank dummy, Foreign_Bank = Foreign-owned bank dummy, Subsidiary = Subsidiary dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank
= Middle-aged bank dummy, Domestic_Interest_Rate = Domestic interest rate, Islamic_Bank × Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = Hirschman-Herfindahl
index, GDP_Per_Capita = GDP per capita, GDP_Per_Capita_Growth = Annual growth rate of GDP_Per_Capita.
In columns (1) and (2), we split the sample into two groups: Observations in countries with at least 90% Muslims in their population are classified as one group (“Muslim+90”) and the rest are placed in the other group
(“Muslim-90”). Muslim+90 and Muslim-90 are the upper half and lower half of the observations sorted based on the Muslim population. In columns (3) and (4), we investigate whether credit risk of Islamic banks is more
or less sensitive to interest rate compared conventional banks, by adding the interaction term of Islamic_Bank and Domestic_Interest_Rate (Islamic_Bank × Domestic_Interest_Rate) to the Muslim+90 and Muslim-90 subsamples. In columns (5) to (8), we split the full sample into four sub-samples: high leveraged banks in Muslim+90, low leveraged banks in Muslim+90, high leveraged banks in Muslim-90 and low leveraged banks in
Muslim-90. In columns (9) to (12), we split the sample into four sub-samples: high leveraged small banks, low leveraged small banks, high leveraged large banks and low leveraged large banks. In columns (13) and (14),
we estimate the model, using the pre-crisis period (2003-2007) and the crisis period (2008-2009) sub-samples.
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Year and country dummies are included in the model, but not
reported in the table. Robust z-statistics are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
44
Annex 6. Insolvency Risk Model
This table presents the estimation of insolvency risk model, using different sub-samples: Muslim+90/Muslim-90, small/large banks and pre-crisis/the crisis periods.
(1)
(2)
(3)
(4)
Muslim+90
Muslim-90
Muslim+90
Muslim-90
(12)
(13)
(14)
Pre-Crisis
Period
Full Sample
Crisis Period
Small Banks
Zscore_rw
Zscore_rw
Zscore_rw
Zscore_rw
Zscore_rw
Zscore_P1_rw
Zscore_P2_rw
Zscore_rw
Zscore_P1_rw
Large Banks
Zscore_P2_rw
Zscore_rw
Zscore_rw
Zscore_rw
Islamic_Bank (β1)
0.317
(1.51)
-0.080
(-0.35)
0.516
(1.48)
0.001
(0.00)
0.388**
(2.12)
0.193
(1.23)
0.352*
(1.84)
-0.131
(-0.62)
Zscore_rw
-0.072
(-0.43)
-0.023
(-0.11)
0.175
(1.00)
-0.426*
(-1.69)
0.345
(0.70)
-0.677**
(-2.15)
Islamic_Window_Bank (β2)
0.248
(1.04)
0.193
(0.77)
0.258
(1.08)
0.197
(0.78)
0.206
(0.86)
0.339
(1.63)
0.218
(0.88)
0.154
(0.83)
0.232
(1.33)
0.287
(1.52)
0.100
(0.57)
0.365
(1.54)
0.018
(0.04)
0.483*
(1.66)
Size (β3)
-0.071*
(-1.67)
-0.045
(-0.91)
-0.069
(-1.63)
-0.049
(-0.93)
-0.052
(-0.84)
0.155***
(2.68)
-0.068
(-1.05)
-0.126***
(-2.74)
-0.014
(-0.34)
-0.141***
(-3.00)
-0.042
(-1.30)
0.162
(0.89)
-0.726**
(-2.10)
0.229
(0.99)
Market_Share (β4)
-0.120
(-1.23)
0.006
(0.05)
-0.121
(-1.26)
0.020
(0.16)
-0.161*
(-1.82)
-0.067
(-0.73)
-0.169*
(-1.77)
0.120
(0.85)
0.015
(0.13)
0.107
(0.71)
-0.055
(-0.68)
-1.146
(-1.58)
0.612
(0.48)
-1.540
(-1.64)
Loan_Total_Earning_Asset_Ratio (β5)
0.001
(0.55)
0.000
(0.00)
0.002
(0.57)
0.000
(0.03)
0.002
(0.93)
0.002
(0.86)
0.002
(0.87)
0.001
(0.27)
0.002
(0.72)
0.001
(0.23)
0.002
(0.87)
-0.003
(-0.85)
-0.007
(-1.43)
-0.001
(-0.26)
Asset_Growth (β6)
0.000
(0.17)
0.001
(0.62)
0.000
(0.19)
0.001
(0.64)
-0.000
(-0.18)
-0.001
(-0.80)
-0.000
(-0.04)
0.002
(1.26)
0.001
(0.90)
0.002
(1.15)
-0.001
(-0.33)
0.001
(0.58)
0.001
(0.45)
0.002
(0.81)
-0.004*
(-1.66)
-0.006
(-1.51)
-0.004
(-1.59)
-0.006
(-1.51)
-0.005
(-1.39)
-0.006
(-1.49)
-0.003
(-0.82)
-0.004
(-1.40)
-0.007**
(-2.45)
-0.007**
(-2.11)
-0.003
(-1.14)
0.004
(0.93)
0.004
(0.69)
0.003
(0.50)
-0.010***
(-4.39)
-0.008***
(-3.72)
-0.010***
(-4.38)
-0.008***
(-3.72)
-0.011***
(-5.49)
-0.019***
(-6.02)
-0.010***
(-5.13)
-0.007***
(-2.70)
-0.017***
(-4.98)
-0.007***
(-3.10)
-0.011***
(-4.38)
-0.004
(-1.14)
0.002
(0.33)
-0.007*
(-1.91)
State_Bank (β9)
0.007
(0.03)
0.237
(1.32)
-0.003
(-0.01)
0.233
(1.30)
0.356*
(1.76)
0.193
(1.07)
0.362*
(1.69)
0.173
(0.96)
0.035
(0.23)
0.207
(1.08)
0.235
(1.47)
-0.190
(-0.78)
0.120
(0.24)
-0.435
(-1.49)
Foreign_Bank (β10)
-0.308
(-1.32)
0.018
(0.09)
-0.314
(-1.34)
0.022
(0.10)
-0.357**
(-1.99)
-0.212
(-1.14)
-0.333*
(-1.84)
0.074
(0.32)
-0.141
(-0.72)
0.138
(0.59)
-0.109
(-0.55)
0.127
(0.41)
-0.356
(-0.87)
0.334
(0.86)
Subsidiary (β11)
-0.189
(-1.18)
-0.371**
(-2.12)
-0.180
(-1.12)
-0.371**
(-2.12)
-0.392**
(-2.12)
-0.308
(-1.36)
-0.442**
(-2.11)
-0.131
(-0.89)
-0.035
(-0.25)
-0.067
(-0.46)
-0.191
(-1.23)
-0.586***
(-3.15)
-0.898***
(-2.73)
-0.592**
(-2.53)
Young_Bank (β12)
-0.672**
(-2.35)
-0.101
(-0.33)
-0.673**
(-2.35)
-0.093
(-0.31)
-0.107
(-0.44)
-0.688**
(-2.24)
-0.003
(-0.01)
-0.509
(-1.32)
-0.619*
(-1.74)
-0.323
(-0.91)
-0.486
(-1.61)
-0.456
(-1.23)
0.000
(.)
-0.351
(-0.68)
Middle_Aged_Bank (β13)
-0.378*
(-1.89)
-0.002
(-0.01)
-0.374*
(-1.87)
-0.006
(-0.04)
-0.096
(-0.63)
0.231
(1.29)
-0.146
(-0.96)
-0.078
(-0.34)
-0.207
(-0.96)
-0.020
(-0.10)
-0.233
(-1.34)
0.093
(0.37)
0.023
(0.05)
0.037
(0.11)
-0.054***
(-4.96)
-0.007
(-0.18)
-0.054***
(-4.98)
-0.005
(-0.13)
-0.014
(-0.53)
0.009
(0.34)
-0.008
(-0.31)
-0.060***
(-4.70)
-0.047***
(-3.42)
-0.060***
(-5.84)
-0.052***
(-3.36)
0.051
(0.49)
-0.077
(-0.51)
0.098
(0.67)
-0.032
(-0.81)
-0.017
(-0.29)
Variables
Noninterest_Income (β7)
Cost_Inefficiency (β8)
Domestic_Interest_Rate (β15)
Islamic_Bank × Domestic_Interest_Rate (βID)
(5)
(6)
(7)
(8)
Small Banks
(9)
(10)
Large Banks
(11)
HHI (β16)
-0.785
(-1.08)
-2.149**
(-2.41)
-0.780
(-1.07)
-2.152**
(-2.41)
0.068
(0.10)
0.037
(0.03)
-0.212
(-0.31)
-1.915**
(-2.00)
-1.802**
(-2.35)
-2.135**
(-2.09)
-0.816
(-1.09)
-0.343
(-0.08)
-6.343
(-0.90)
-1.624
(-0.27)
GDP_Per_Capita (β17)
-0.042
(-1.19)
-0.099***
(-2.72)
-0.042
(-1.17)
-0.099***
(-2.70)
-0.034
(-0.86)
-0.048*
(-1.72)
-0.040
(-1.01)
-0.072**
(-2.39)
-0.041
(-1.64)
-0.080**
(-2.55)
-0.067*
(-1.68)
-0.138
(-1.22)
-0.261
(-1.22)
-0.128
(-0.90)
GDP_Per_Capita_Growth(β18)
-0.013
(-1.11)
0.003
(0.15)
-0.014
(-1.13)
0.003
(0.14)
-0.009
(-0.75)
0.001
(0.07)
-0.014
(-1.16)
0.002
(0.11)
0.003
(0.14)
0.002
(0.13)
-0.016
(-1.38)
0.054
(0.95)
0.009
(0.19)
0.073
(0.81)
Constant (β0)
0.000
(.)
5.983***
(7.05)
7.529***
(5.04)
6.028***
(6.88)
5.235***
(4.97)
0.504
(0.52)
5.254***
(4.75)
7.395***
(8.57)
3.350***
(4.24)
7.586***
(8.69)
0.000
(.)
2.069
(0.48)
18.794***
(2.66)
0.696
(0.13)
Number of Obs
R-squared
839
0.205
1,071
0.141
839
0.204
1,071
0.141
896
0.206
841
0.251
902
0.207
1,014
0.191
972
0.252
1,029
0.215
984
0.174
441
0.242
145
0.379
296
0.315
26.22***
4.62**
0.12
0.12
H0: β 15 = β ID = 0 (F-stat.)
H0: β 15 + β ID = 0 (F-stat.)
45
Dependent Variables: Zscore_rw = Logarithm of rolling-window Zscore which is equal to (ROAA+ Capital_Asset_Ratio)/SDROAA_rw, ROAA = Return on average assets, Capital_Asset_Ratio = Equity capital to asset ratio, SDROAA_rw = Standard
deviation of ROAA over 3 years (current year and two previous consecutive years). Banks need to have three consecutive observations. Acquiring banks are excluded from the sample, since the volatility on their assets returns can be due to the
acquisition. Zscore_P1_rw = Logarithm of ROAA/SDROAA3_rw, Zscore_P2_rw = Logarithm of Capital_Asset_Ratio/SDROAA3_rw.
Explanatory Variables: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets, Market_Share = Logarithm of market share of total assets, orthogonalized on Size,
Loan_Total_Earning_Asset_Ratio = Share of net loans in total earning assets, Asset_Growth = Annual growth rate of total assets, Noninterest_Income = Share of non-interest income in total operating income, Cost_Inefficiency = Cost to income ratio,
State_Bank = State-owned bank dummy, Foreign_Bank = Foreign-owned bank dummy, Subsidiary = Subsidiary dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank = Middle-aged bank dummy, Muslim_Share = Share of Muslims in
population, Domestic_Interest_Rate = Domestic interest rate, Islamic_Bank × Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = Hirschman-Herfindahl index, GDP_Per_Capita = GDP per capita,
GDP_Per_Capita_Growth = Annual growth rate of GDP_Per_Capita.
In columns (1) and (2), we split the sample into two groups: Observations in countries with at least 90% Muslims in their population are classified as one group (“Muslim+90”) and the rest are placed in the other group (“Muslim-90”). Muslim+90 and
Muslim-90 are the upper half and lower half of the observations sorted based on the Muslim population. In columns (3) to (4), we investigate whether insolvency risk of Islamic banks is more or less sensitive to interest rate compared conventional
banks, by adding the interaction term of Islamic_Bank and Domestic_Interest_Rate (Islamic_Bank × Domestic_Interest_Rate) to the Muslim+90 and Muslim-90 sub-samples. In columns (5) to (7), we estimate insolvency risk model on the small
banks sub-sample, using Zscore_rw, Zscore_P1_rw and Zscore_P2_rw as the dependent variables, respectively. In columns (8) to (10), we estimate insolvency risk model on the large banks sub-sample, using Zscore_rw, Zscore_P1_rw and
Zscore_P2_rw as the dependent variables, respectively. In columns (11) and (12), we estimate the model, using Zscore_rw as the dependent variable and the pre-crisis period (2003-2007) and the crisis period (2008-2009) sub-samples. In columns
(13) and (14), we estimate the model, using the small and large banks sub-samples during the crisis period (2008-2009).
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Year and country dummies are included in the model, but not reported in the table. Robust zstatistics are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
46
Annex 7. Bank Interest Rate Model
This table presents the estimation of bank interest rate model. We investigate the sensitivity of interest income and expense of Islamic banks to domestic interest rate
during the pre-crisis period (columns 1-5) and the crisis-period (columns 6-10).
Pre-Crisis Period (2003-2007)
Crisis Period (2008-2009)
(1)
Net_Interest_Margin
(A)
(2)
Interest_Income_Rate
(B)
(3)
Interest_Expense_Rate
(C)
(4)
Loan_Rate
(D)
(5)
Deposit_Rate
(E)
(6)
A
(7)
B
(8)
C
(9)
D
(10)
E
Islamic_Bank (γ1)
0.159
(0.46)
0.009
(0.02)
-0.216
(-0.61)
0.361
(0.33)
0.451
(0.68)
2.141***
(3.52)
1.776**
(2.27)
0.563
(0.93)
2.399***
(2.75)
0.908
(1.59)
Islamic_Window_Bank (γ2)
0.128
(0.51)
-0.039
(-0.76)
0.115
(0.34)
-0.091
(-1.35)
0.043
(0.19)
0.019
(0.39)
0.500
(0.62)
-0.113
(-0.87)
0.034
(0.07)
-0.052
(-0.60)
0.760**
(2.13)
0.151
(0.81)
0.645
(1.29)
1.226***
(2.93)
0.312
(0.84)
1.166**
(2.49)
0.669
(1.03)
0.766*
(1.80)
-0.038
(-0.10)
1.142***
(2.68)
0.033
(0.19)
0.268
(1.21)
-0.067
(-0.51)
-0.624
(-1.21)
-0.092
(-0.33)
-0.510
(-0.74)
-5.549***
(-3.45)
-5.204***
(-2.69)
-3.610**
(-2.13)
-5.098***
(-3.01)
0.009
(1.38)
-0.018***
(-4.86)
-0.007
(-0.88)
-0.010**
(-2.29)
-0.008
(-1.07)
0.012***
(3.22)
-0.011
(-0.74)
0.005
(0.60)
-0.005
(-0.42)
-0.003
(-0.39)
0.004
(0.49)
-0.007
(-1.34)
-0.022*
(-1.68)
0.000
(0.01)
-0.030***
(-2.98)
0.017
(1.06)
-0.015
(-0.87)
0.008
(0.65)
-0.023***
(-2.64)
0.008
(1.05)
-0.017***
(-6.13)
0.002
(0.24)
-0.131
(-0.48)
-0.013***
(-3.75)
0.004
(0.39)
-0.279
(-0.72)
0.000
(0.13)
0.004
(0.59)
0.013
(0.03)
-0.013**
(-2.09)
-0.027
(-1.20)
0.514
(0.76)
-0.003
(-0.48)
-0.001
(-0.04)
-0.381
(-0.60)
-0.007*
(-1.86)
0.014
(1.49)
0.176
(0.41)
0.003
(0.72)
0.010
(0.85)
-0.228
(-0.46)
0.010***
(2.69)
0.003
(0.28)
0.296
(0.43)
0.014**
(2.35)
0.005
(0.28)
-0.373
(-0.64)
0.017***
(3.30)
0.005
(0.51)
-0.149
(-0.31)
-0.515**
(-2.27)
0.045
(0.22)
0.393
(0.59)
-0.823**
(-1.99)
-1.102***
(-3.74)
0.399
(0.56)
-0.448
(-1.10)
-1.037***
(-4.91)
0.646*
(1.67)
1.792
(1.33)
1.637*
(1.79)
1.561
(1.22)
0.156
(0.18)
-0.462
(-0.89)
1.333
(1.63)
-0.010
(-0.03)
0.624*
(1.84)
-0.436
(-0.78)
-0.670
(-1.32)
-0.408
(-0.81)
-0.789
(-1.07)
-0.436
(-0.96)
-0.665**
(-2.20)
0.982
(0.93)
-1.767***
(-2.85)
-1.280**
(-2.41)
-0.684
(-0.45)
0.150
(0.34)
-0.837***
(-2.69)
1.933
(1.24)
0.559
(1.50)
0.027
(1.16)
0.835*
(1.86)
0.162***
(6.92)
0.605*
(1.95)
0.098***
(5.65)
1.617**
(2.41)
0.120*
(1.70)
1.184***
(3.05)
0.101**
(2.17)
0.364
(0.80)
-0.010
(-0.10)
1.347
(1.55)
0.676***
(4.33)
0.083
(0.22)
0.625***
(4.23)
0.296
(0.55)
0.389*
(1.91)
0.386
(0.96)
0.636***
(3.01)
-0.014
(-0.32)
-0.726
(-0.60)
-0.076
(-1.21)
-1.951
(-1.22)
-0.016
(-0.29)
0.122
(0.12)
-0.127
(-1.26)
0.756
(0.20)
-0.141**
(-2.29)
-2.154
(-1.17)
-0.211**
(-2.07)
0.245
(0.08)
-0.345***
(-2.61)
10.855*
(1.83)
-0.094
(-0.63)
9.682*
(1.74)
-0.377**
(-2.29)
-5.170
(-0.48)
-0.271**
(-2.03)
0.772
(0.11)
0.012
(0.37)
0.106***
(2.88)
0.118***
(2.65)
0.180*
(1.80)
0.237***
(2.84)
-0.005
(-0.06)
0.190
(1.57)
0.142
(1.40)
-0.136
(-0.53)
-0.046
(-0.28)
-0.026
(-1.09)
4.760***
(4.68)
0.013
(0.36)
7.877***
(5.77)
0.071
(1.45)
0.000
(.)
-0.017
(-0.40)
5.835**
(1.97)
-0.025
(-0.99)
0.613
(0.35)
0.026
(0.81)
-0.204
(-0.05)
0.109
(1.53)
-23.974**
(-2.55)
0.106
(1.27)
0.000
(.)
0.188**
(2.00)
-9.910
(-1.03)
0.196*
(1.91)
-22.785**
(-2.35)
Number of Obs
R-squared
1,036
0.524
1,030
0.640
1,014
0.530
333
0.583
290
0.751
471
0.421
471
0.538
463
0.573
382
0.621
353
0.672
H0: γ 15 = γ ID = 0 (F-stat.)
H0: γ 15 + γ ID = 0 (F-stat.)
1.44
0.07
49.53***
1.65
34.14***
1.67
4.06
0.00
10.72***
0.25
4.78*
3.44*
22.88***
3.00*
17.93***
7.55***
7.75**
0.00
13.37***
2.10
Variables
Size (γ3)
Market_Share (γ4)
Capital_Asset_Ratio (γ 5)
Noninterest_Income (γ 6)
Cost_Inefficiency (γ7)
Loan_Loss_Reserve (γ 8)
State_Bank (γ9)
Foreign_Bank (γ10)
Subsidiary (γ11)
Young_Bank (γ12)
Middle_Aged_Bank (γ 13)
Domestic_Interest_Rate (γ 15)
Islamic_Bank ×
Domestic_Interest_Rate (γ ID)
HHI (γ16)
GDP_Per_Capita (γ17)
GDP_Per_Capita_Growth (γ 18)
Constant (γ 0)
Dependent Variables: Net_Interest_Margin = (Interest Income – Interest Expense) / Average Earning assets, Interest_Income_Rate = Interest income divided by average earning assets for
conventional banks and mark-up income over average earning assets for Islamic banks, Interest_Expense_Rate = Interest expense divided by average interest bearing liabilities and profit
payouts over average profit bearing liabilities for Islamic banks, Loan_Rate = Interest income on loans divided by average gross lending for conventional banks and mark-up income on
lending divided by average gross loans for Islamic banks, Deposit_Rate = Interest expense on customer deposit divided by average customer deposits for conventional banks and profit
payouts on customer deposits divided by average customer deposits for Islamic banks.
Explanatory Variables: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets, Market_Share = Logarithm of
market share of total assets, orthogonalized on Size, Capital_Asset_Ratio = The ratio of equity capital on total assets, Noninterest_Income = Share of non-interest income in total operating
income, Cost_Inefficiency = Cost to income ratio, Loan_Loss_Reserve = Loan loss reserves on gross loans ratio, State_Bank = State-owned bank dummy, Foreign_Bank = Foreign-owned
bank dummy, Subsidiary = Subsidiary dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank = Middle-aged bank dummy, Domestic_Interest_Rate = Domestic interest rate,
Islamic_Bank × Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = Hirschman-Herfindahl index, GDP_Per_Capita = GDP per capita,
GDP_Per_Capita_Growth = Annual growth rate of GDP_Per_Capita.
In columns (1) to (5), we estimate the model using Net_Interest_Margin, Interest_Income_Rate, Interest_Expense_Rate, Loan_Rate and Deposit_Rate as the dependent variables
respectively for the pre-crisis period (2003-2007). In columns (6) to (10), we estimate the model for the crisis period (2008-2009).
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Year and country dummies are
included in the model, but not reported in the table. Robust z-statistics are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
47
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52
Table I. Existing Literature
This table presents a summary of selected empirical literature on Islamic banking.
Authors
Country(ies) of Study
Period
Data Type
Research Focus
Methodology
Main Finding
Asset size and bank
performance
Regression - OLS
Larger banks are more profitable yet have higher leverage. Analysis is based on
only two Islamic banks.
Bashir (1999)
Sudan
1979-1993
Yearly bank-level
accounting data
Samad (1999)
Malaysia
1992-1996
Yearly bank-level
accounting data
Cost efficiency
Descriptive statistics
and ANOVA
Islamic banks are more efficient than their conventional counterparts.
El-Gamal and
Inanoglu (2002)
Turkey
1990-2000
Yearly bank level
accounting data
Production technology
Stochastic Frontier
Analysis
Islamic banks have a similar production technology to conventional commercial
banks.
Majid et al (2003)
Malaysia
1993-2000
Yearly bank level
accounting data
Cost efficiency
Stochastic Frontier
Analysis
No statistically significant difference in the level of efficiency between Islamic
and conventional banks and no evidence to suggest that ownership influences cost
efficiency.
Hassan and Bashir
(2003)
Islamic banks operating
in 21 countries
1994-2001
Yearly bank level
accounting data
Determinants of bank
profitability (ROA,
ROE, NIM)
Regression - GLS
Controlling for macroeconomic environment, financial market structure, and
taxation, the results indicate that high capital and loan-to-asset ratios lead to
higher profitability (as does favourable macroeconomic conditions).
Yudistra (2004)
Islamic banks operating
in 12countries
1997-2000
Yearly bank level
accounting data
Technical and scale
efficiency
Data Envelopment
Analysis (DEA) and
OLS regression
Islamic bank inefficiencies appear relatively low (around 10%) compared with
those for conventional banks derived from other studies. Small to medium-sized
Islamic banks exhibit diseconomies of scale. Islamic banks in the Middle East are
less efficient than those operating outside the region.
Al-Jarrah and
Molyneux (2005)
Bahrian, Eqypt, Jordan
and Saudia Arabia
1992-2000
Yearly bank level
accounting data
Cost and profit
efficiency
Stochastic Frontier
Analysis
Islamic banks are found to be the most cost and profit efficient banks compared to
conventional commercial and investment banks.
Mohamad et al. (2008)
21 Organization of
Islamic Conference
(OIC) countries
1990-2005
Yearly bank level
accounting data
Cost and profit
efficiency
Stochastic Frontier
Analysis
No significant difference between cost and profit efficiency of conventional
versus Islamic banks, irrespective of size, age and geographical location Islamic
banks based in the Middle East and Turkey are more cost efficient than their
African counterparts.
Bader et al. (2008)
21 OIC countries
1995-2005
Yearly bank level
accounting data
Cost, revenue and
profit efficiency
Data Envelopment
Analysis
No significant difference between cost, revenue and profit efficiency of
conventional versus Islamic banks. Note this study uses the same sample as
Mohamed et al (2008).
Chong and Liu (2009)
Malaysia
1995:04 –
2004:04
Monthly interest
rates (rates of
return for Islamic
banks)
Causality relationship
between Islamic banks
deposits rates and
interest rates in
conventional banking.
Granger causality test
Rates of return on the investment deposits of Islamic banks are closely related to
rates on conventional banks’ deposits.
Abdul-Majid et al.
(2010)
10 countries
1996-2002
Yearly bank level
accounting data
Returns to scale and
efficiency
Parametric output
distance function
Islamic banks are found to have moderately higher returns to scale than
conventional banks but appear less efficient due to Sharia compliance. Country
effects have a significant impact on efficiency differences.
Baele, Farooq and
Ongena (2010)
Pakistan
2006:04 –
2008:12
Monthly business
loans
Loan default rate
Hazard function
Default rates on Islamic loans are lower than for conventional loans.
Beck, Demirgüç-Kunt
and Merrouche (2010)
141 countries
(including 22 OIC
member countries)
1995 2007
Yearly bank-level
accounting data
Efficiency, asset
quality, stability and
business orientation
Regression – OLS
Fixed effects, Robust
Few significant differences are found between Islamic and conventional banks.
Čihák and Hesse
(2010)
20 OIC member
countries
1993-2004
Yearly bank-level
accounting data
Insolvency risk
Regression – OLS and
Robust
Small Islamic banks are more stable than small conventional banks; however,
large Islamic banks are less stable than their conventional counter-parts.
53
Imam and Kpodar
(2010)
117 countries
1992-2006
Country level data
Determinants of the
diffusion of Islamic
banking
Regression - Tobit
Probability for Islamic banking to develop in a country rises with the share of the
Muslim population, income per capita, and whether the country is a net exporter
of oil. Increasing interest rates limit the diffusion of Islamic banking.
Rashwan (2010)
15 countries
2007-2009
Bank level data
Profitability and
efficiency over the
banking crisis
Multivariate analysis of
variance (MANOVA)
Islamic banks are more profitable and efficient than traditional banks pre-crisis
but the opposite is the case post-crisis.
Ongena and ŞendenizYüncü (2010)
Turkey
2008
Bank-firm
relationships
Firm bank choice
Multinomial logit
Islamic banks mainly have corporate clients that are young, transparent, industryfocused, and have multiple-bank relationships.
Hasan and Dridi
(2010)
8 countries
2007-2009
Yearly bank-level
accounting data
Factors influencing
performance, growth
and ratings over crisis
period
Regression – OLS
The credit and asset growth of Islamic banks was more than that of conventional
banks from 2008 to 2009 ‘contributing to financial and economic stability’,
although profits of Islamic banks fell more than conventional banks in 2009 due
to limitations in their risk management practices
Weill (2011)
17 OIC member
countries
2001 –
2007
Yearly bank-level
accounting data
Market power
Regression – random
effects GLS
Islamic banks have lower market power than conventional banks.
Yearly bank-level
accounting data
Credit risk, insolvency
risk, interest rate risk
and possibility of
extracting religious rent
Regression – random
effects
Islamic banks that are small, leveraged or based in countries with predominantly
Muslim populations have lower credit risk than conventional banks. Small Islamic
banks appear more stable than similar sized conventional banks. During the recent
crisis, however, large Islamic banks exhibit lower stability than large conventional
banks. Implicit interest income and expense, as well as credit risk of Islamic
banks are less responsive to domestic interest rates. Islamic banks do not seem to
charge special rents to their clients for offering Shariá compliant financial
products.
This Paper
24 OIC member
countries
1999-2009
54
Figure 1. Depositors’ Payoff in Islamic and Conventional Banking
This figure illustrates the payoffs from investment account depositors in Islamic banking versus time depositors in conventional
banking.
Depositors’
Payoff
Depletion of
Islamic Bank’s
Banks’
Capital
Depletion of
Conventional Bank’s
Banks’
Capital
Fiduciary
Fiduciary
Risk
Risk
Theory of Islamic
Banking
Islamic Banking
in Practice
Conventional
Banking
α
α
Earnings
B
P
Displaced Commercial
Commercial Risk
Displaced
Risk
L
The horizontal axis represents a bank’s earnings before paying interest expense. The vertical axis shows the interest expense to be paid to
depositors (depositors’ payoff). A conventional bank incurs loss for any earnings less than B, where the earnings equal to the interest expense.
Depositors of conventional banks receive interest irrespective of the realized earnings, to the extent that the possible loss does not completely
deplete the capital. Hence, the ex-post relationship between earnings and depositors’ payoff is depicted by the horizontal line (earnings and
depositors’ payoffs are positively correlated in the ex-ante relationship, since depositors demand higher payoffs from banks with higher
expected earnings, as they are expected to be more risky). The figure shows that the depletion occurs when earnings are negative; however, in
reality depletion can happen when earnings are positive.
In theory, the realized profit or loss should be shared between depositors and equity-holders. The dashed line with a slope less than 45 degrees
(α) shows that depositors payoff is proportionate to realized performance; however, in practice there is substantial evidence that Islamic banks
pay a competitive rate of return, irrespective of actual performance. Also Islamic banks may adjust profit rates upward but at a slower rate than
realized profitability so as to limit the level and volatility of deposit payoffs. At the time of crisis, however, Islamic banks may share the
realized loss with investment account holders to avoid insolvency (the bold line is simply illustrative and does not necessarily show the real
scale and magnitude of divergence from conventional depositors’ payoffs). This suggests that Islamic banks may have a greater capacity to
bear losses compared to conventional banks. The magnitude of the extra capacity (and hence the exact position of the vertical line that
illustrates the capital depletion of a typical Islamic bank) depends on the weight of investment deposits in the total funding of the Islamic bank.
Implicitly, investment account holders own a bond, a long position on a call option and a short position on a put option. The strike price of the
call is determined arbitrarily by Islamic banks, and the strike price of the put is determined based on the degree of deposit market competition,
level of incurred losses and capital strength. Overall, when Islamic banks are profitable investment account holders may get P over the
depositor payoffs at conventional banks, at the expense of L in the case of a scenario where losses occur. Hence, in practice the difference
between depositors’ payoffs of Islamic versus conventional banks can appear mostly in the tails distribution of bank’s earnings. Displaced
commercial risk illustrates the situation where equity-holders have to transfer (or sacrifices) a part of their profit or incur a portion of
depositors’ loss to avoid deposits withdrawal. Fiduciary risk is the risk associated with Islamic banks deviating from Shariá principles in
sharing returns between investment account holders and equity-holders. It may be that depositors do not have the relevant incentives or/and
expertise to observe or take action against such deviations.
55
Table II. Descriptive Statistics
General descriptive statistics and risk measure variables for Islamic, conventional and Islamic window banks over the 1999-2009 period.
Islamic Banks
General Descriptive Statistics
Bank_Interest_Rate Proxies
Insolvency_Risk Proxies
Credit_Risk
Proxies
Variables
Number
Mean
SD
Loan_Loss_Reserve (%)
593
6.75
7.71
Impaired_Loans (%)
381
8.31
10.33
Loan_Loss_Provision (%)
574
1.35
3.10
Zscore_rw
388
3.42
Zscore_P1_rw
411
Zscore_P2_rw
389
Conventional Banks
Min
Mean
SD
Min
Islamic Window Banks
Max
Number
Max
T-Stat.†
Number
Mean
SD
Min
Max
0.00
58.00
2,105
8.72
9.38
0.00
60.55
-5.23***
561
7.82
7.62
0.00
51.67
0.00
66.39
1,604
11.14
12.97
0.00
76.41
-4.55***
467
10.23
10.97
0.00
67.93
-22.20
26.00
1,982
1.70
3.18
-22.52
30.69
-2.33**
537
1.65
3.06
-4.30
30.70
1.31
-0.74
8.59
1,349
3.48
1.30
-1.32
8.72
-0.83
392
3.54
1.36
-1.45
9.39
1.13
1.11
-3.28
5.33
1,42
1.25
1.32
-4.84
5.39
-1.88*
417
1.56
1.33
-5.13
5.70
3.28
1.37
-0.50
8.55
1,367
3.31
1.36
-1.31
8.70
-0.42
395
3.37
1.36
-0.44
9.25
Zscore
75
2.87
0.93
0.05
5.22
251
2.87
1.02
-0.77
6.31
0.02
67
2.85
1.08
-0.15
5.13
Zscore_P1
70
0.53
0.75
-2.40
2.04
226
0.65
0.91
-2.53
2.62
-1.15
60
0.95
0.92
-1.60
2.86
Zscore_P2
75
2.77
0.92
0.70
5.20
252
2.76
1.00
0.09
6.28
0.09
67
2.73
1.08
-0.04
5.03
Net_Interest_Margin (%)
684
4.19
3.39
-9.42
24.09
2,46
4.17
3.09
-12.58
24.83
0.19
673
3.47
2.43
-10.45
23.45
Interest_Income_Rate (%)
623
8.02
4.38
0.01
38.70
2,351
9.81
4.84
0.09
39.07
-8.87***
650
8.05
3.43
1.11
31.81
Interest_Expense_Rate (%)
544
4.39
3.39
0.08
26.40
2,355
5.84
3.67
0.06
26.55
-8.80***
649
4.65
2.59
0.30
19.41
Loan_Rate (%)
228
9.60
4.70
0.30
23.80
629
9.97
4.46
0.60
29.60
-1.03
209
8.90
4.44
1.70
28.64
Deposit_Rate (%)
188
5.02
3.75
0.10
16.50
588
4.81
3.07
0.10
15.30
0.68
180
4.27
2.46
0.80
11.80
Total Assets (mil. $)
782
3,732
7,284
530
48,1
2,448
4,041
8,664
132
87,9
-1
640
5,188
8,576
4,478
63
Market_Share (%)
782
0.07
0.13
0.00
1.00
2,448
0.06
0.12
0.00
1.00
2.26**
640
0.07
0.11
0.00
0.56
Capital_Asset_Ratio (%)
750
17.10
16.12
0.43
87.01
2,403
13.38
11.07
0.01
86.93
5.90***
626
11.78
9.28
1.48
70.12
ROAA (%)
715
1.48
2.46
-12.29
13.20
2,458
1.23
2.26
-16.48
13.89
2.40**
672
1.28
2.04
-17.82
8.93
ROAE (%)
715
12.93
17.43
-118.28
123.65
2,429
12.26
18.17
-124.83
133.30
0.90
668
15.55
17.42
-118.25
119.92
Loan_Total_Earning_Asset_Ratio
(%)
767
58.00
26.01
0.02
100.00
2,431
53.58
22.78
0.00
100.00
4.22***
637
57.12
20.43
0.00
100.00
Loan_Growth (%)
685
29.59
51.33
-100.00
351.73
2,047
21.67
40.31
-100.00
325.28
3.68***
573
19.90
38.83
-96.60
326.93
Asset_Growth (%)
709
26.27
33.45
-74.95
207.08
2,265
19.16
30.22
-73.80
211.08
5.06***
596
18.68
27.97
-62.00
177.60
Noninterest_Income (%)
689
42.14
29.34
-70.23
158.92
2,405
33.60
23.17
-115.15
158.45
7.03***
668
33.33
19.89
-20.96
149.22
Cost_Inefficiency (%)
658
59.80
34.03
3.04
268.53
2,382
57.12
31.56
1.88
287.87
1.82*
661
48.08
21.69
3.93
180.00
Credit_Risk Proxies: Loan_Loss_Reserve = Loan loss reserves on gross loans ratio, Impaired_Loans = Impaired loans on gross loans ratio, Loan_Loss_Provision = Loan loss
provision on average gross loans ratio.
Insolvency_Risk Proxies: Zscore_rw = Logarithm of rolling-window Zscore which is equal to (ROAA+ Capital_Asset_Ratio)/SDROAA_rw, SDROAA_rw = Standard
deviation of ROAA over 3 years (current year and two previous consecutive years). Banks need to have three consecutive observations. Acquiring banks are excluded from
the sample, since the volatility on their assets returns can be due to the acquisition. Zscore_P1_rw = Logarithm of ROAA/SDROAA3_rw, Zscore_P2_rw = Logarithm of
Capital_Asset_Ratio/SDROAA3_rw, Zscore = Logarithm of (M_ROAA+M_Capital_Asset_Ratio)/SDROAA, M_ROAA = Mean of ROAA over the sample period,
M_Capital_Asset_Ratio =Mean of Capital_Asset_Ratio over the sample period, SDROAA = standard deviation of ROAA over the sample period (banks needs to have at least
four consecutive observations), Zscore_P1 = Logarithm of M_ROAA/SDROAA, Zscore_P2 = Logarithm of M_ETA/SDROAA.
Bank_Interest_Rate Proxies: Net_Interest_Margin = (Interest Income – Interest Expense) / Average Earning assets, Interest_Income_Rate = Interest income divided by
average earning assets for conventional banks and mark-up income over average earning assets for Islamic banks, Interest_Expense_Rate = Interest expense divided by
average interest bearing liabilities and profit payouts over average profit bearing liabilities for Islamic banks, Loan_Rate = Interest income on loans divided by average gross
lending for conventional banks and mark-up income on lending divided by average gross loans for Islamic banks, Deposit_Rate = Interest expense on customer deposit
divided by average customer deposits for conventional banks and profit payouts on customer deposits divided by average customer deposits for Islamic banks.
General Descriptive Statistics: Total Assets = Total assets in millions U.S.$, Market_Share = Market share of total assets, Capital_Asset_Ratio = Equity capital to asset ratio,
ROAA = Return on average assets, ROAE = Return on average equity, Loan_Total_Earning_Asset_Ratio = Share of net loans in total earning assets, Loan_Growth = Annual
growth rate of gross loans, Asset_Growth = Annual growth rate of total assets, Noninterest_Income = Share of non-interest income in total operating income,
Cost_Inefficiency = Cost to income ratio.
† T-Stat. of Mean Equality Test between Islamic and conventional banks. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
56
Table III. Credit Risk Model
This table presents the estimation of the credit risk model. In columns (1) to (6), we investigate whether credit risk of Islamic banks is, on
average, higher or lower than conventional banks. The final five columns investigate various interaction variables highlighting whether
religious factors influence credit risk.
Variables
Islamic_Bank (α1)
Islamic_Window_Bank (α2)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
-2.300***
(-2.98)
-2.035**
(-2.01)
-1.627*
(-1.83)
-0.791
(-1.00)
-0.917***
(-3.84)
-2.088**
(-2.23)
-0.894
(-1.12)
-0.896***
(-3.72)
-2.135**
(-2.28)
-0.890
(-1.11)
-0.887***
(-3.59)
-1.971**
(-2.03)
-0.441
(-0.54)
-0.860***
(-3.29)
-3.037***
(-2.79)
-1.213
(-1.35)
-0.728**
(-2.15)
8.200
(1.55)
-0.579
(-0.71)
-0.750***
(-2.66)
0.985
(0.67)
-1.541*
(-1.84)
-0.764***
(-2.73)
-0.449
(-0.29)
-0.973
(-1.08)
-0.769**
(-2.28)
-5.734***
(-3.69)
-1.349
(-1.49)
-0.823***
(-2.63)
-14.560**
(-2.24)
-1.071
(-1.18)
-0.872**
(-2.42)
-0.108
(-0.20)
-0.076
(-1.53)
0.839*
(1.86)
-0.104
(-0.17)
-0.010
(-0.22)
Size (α 3)
Islamic_Bank × Size (αIS)
Market_Share (α4)
0.373
(1.25)
-0.023
(-0.51)
Capital_Asset_Ratio (α5)
0.390
(1.31)
-0.023
(-0.50)
0.378
(1.25)
-0.023
(-0.51)
0.454
(1.40)
-0.017
(-0.37)
-0.277
(-0.46)
-0.014
(-0.30)
0.077
(0.20)
-0.006
(-0.14)
-0.140
(-0.37)
-0.010
(-0.21)
-0.142
(-0.23)
-0.017
(-0.37)
Islamic_Bank ×
Capital_Asset_Ratio (αIC)
0.175**
(2.33)
Loan_Growth (α6)
-0.030***
(-5.28)
-0.011
(-0.79)
0.005
(0.57)
Noninterest_Income (α7)
Cost_Inefficiency (α8)
State_Bank (α9)
Foreign_Bank (α10)
Subsidiary (α11)
-0.030***
(-5.22)
-0.011
(-0.80)
0.005
(0.58)
-0.030***
(-5.25)
-0.011
(-0.78)
0.005
(0.55)
-0.029***
(-5.05)
-0.007
(-0.55)
0.003
(0.34)
-0.028***
(-4.86)
-0.011
(-0.81)
0.004
(0.47)
-0.028***
(-4.93)
-0.006
(-0.46)
0.003
(0.31)
-0.028***
(-4.96)
-0.009
(-0.67)
0.004
(0.40)
-0.027***
(-4.80)
-0.009
(-0.69)
0.006
(0.65)
-0.027***
(-4.83)
-0.010
(-0.76)
0.005
(0.53)
-0.027***
(-4.79)
-0.011
(-0.80)
0.005
(0.56)
-0.441
(-0.48)
2.408**
(1.97)
-0.971
(-1.02)
-0.410
(-0.45)
2.422**
(1.97)
-0.976
(-1.03)
-0.236
(-0.25)
2.685**
(2.17)
-0.794
(-0.83)
-0.813
(-0.86)
1.787
(1.47)
-0.276
(-0.29)
-0.259
(-0.27)
2.877**
(2.36)
-0.646
(-0.68)
-0.703
(-0.78)
2.068*
(1.73)
-0.549
(-0.59)
-0.874
(-0.92)
1.736
(1.43)
-0.207
(-0.22)
-0.807
(-0.85)
1.649
(1.36)
-0.342
(-0.36)
-0.922
(-0.98)
1.799
(1.48)
-0.333
(-0.35)
0.366
(0.21)
0.187
(0.23)
0.311
(0.18)
0.202
(0.25)
-0.036
(-1.39)
-0.092
(-0.05)
0.180
(0.22)
-0.038
(-0.02)
-0.040
(-0.05)
-0.005
(-0.17)
-0.205
(-0.12)
-0.048
(-0.06)
-0.152
(-0.09)
0.101
(0.12)
-0.397
(-0.22)
0.149
(0.18)
0.262
(0.16)
0.146
(0.18)
0.232**
(2.39)
0.229**
(2.47)
0.209**
(2.25)
Young_Bank (α12)
Middle_Aged_Bank (α13)
Muslim_Share (α14,1)
Islamic_Bank × Muslim_Share (αIM)
-0.120**
(-2.00)
Legal_System (α14,2)
3.627***
(4.22)
-4.701***
(-3.46)
Islamic_Bank × Legal_System (α IL)
Domestic_Interest_Rate (α15)
0.112*
(1.96)
0.210**
(2.26)
0.088
(1.50)
0.127**
(2.11)
Islamic_Bank ×
Domestic_Interest_Rate (αID)
-0.399**
(-2.19)
HHI (α 16)
GDP_Per_Capita (α 17)
GDP_Per_Capita_Growth (α18)
Constant (α0)
Year Dummies
Country Dummies
Number of Obs
R-squared
-2.515
(-0.82)
0.013
(0.45)
-3.727
(-0.98)
0.069
(0.63)
0.193
(0.06)
0.017
(0.59)
-2.771
(-0.82)
-0.003
(-0.11)
-4.037
(-1.07)
0.073
(0.68)
-4.749
(-1.30)
0.031
(0.28)
-3.878
(-1.03)
0.065
(0.60)
0.004
(0.06)
20.123***
(3.58)
0.002
(0.03)
20.189***
(4.43)
0.006
(0.10)
18.516***
(4.51)
0.008
(0.13)
20.169***
(3.64)
0.019
(0.29)
22.796***
(4.42)
0.008
(0.13)
22.011***
(3.77)
9.119***
(21.45)
22.543***
(5.80)
22.337***
(5.70)
22.193***
(5.56)
-0.006
(-0.10)
23.896***
(5.79)
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
3,259
0.007
1,897
0.076
1,897
0.085
1,897
0.085
1,897
0.084
1,897
0.182
1,897
0.093
1,897
0.123
1,897
0.186
1,897
0.186
1,897
0.182
H0: α 14,1 = αIM = 0 (F-stat.)
H0: α 14,1 + αIM = 0 (F-stat.)
5.17*
5.17**
H0: α 14,2 = αIL = 0 (F-stat.)
H0: α 14,2 + αIL = 0 (F-stat.)
19.75***
0.87
H0: α 15 = αID = 0 (F-stat.)
H0: α 15 + αID = 0 (F-stat.)
9.07**
0.76
H0: α 5 = α IC = 0 (F-stat.)
H0: α 5 + αIC = 0 (F-stat.)
5.49*
2.62
H0: α 3 = α IS = 0 (F-stat.)
H0: α 3 + αIS = 0 (F-stat.)
6.43**
0.01
Dependent Variable: The ratio of loan loss reserves on gross loans is used as the credit risk proxy.
Explanatory Variables: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets,
Islamic_Bank × Size = Interaction term of Islamic_Bank and Size, Market_Share = Logarithm of market share of total assets, orthogonalized on Size,
Capital_Asset_Ratio = The ratio of equity capital on total assets, Islamic_Bank × Capital_Asset_Ratio = Interaction term of Islamic_Bank and
Capital_Asset_Ratio, Loan_Growth = Annual growth rate of gross loans, Noninterest_Income = Share of non-interest income in total operating income,
Cost_Inefficiency = Cost to income ratio, State_Bank = State-owned bank dummy, Foreign_Bank = Foreign-owned bank dummy, Subsidiary = Subsidiary
57
dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank = Middle-aged bank dummy, Muslim_Share = Share of Muslims in population, Islamic_Bank
× Muslim_Share = Interaction term of Islamic_Bank and Muslim_Share, Legal_System = Takes the value of zero, if the country does not use Shariá law to define
its legal system, the value one for countries which consider Shariá together with other legal systems, and has the value two if the legal system is based
exclusively on Shariá law, Islamic_Bank × Legal_System = Interaction term of Islamic_Bank and Legal_System, Domestic_Interest_Rate = Domestic interest
rate, Islamic_Bank × Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = Hirschman-Herfindahl index, in column
(8) we use its orthogonalized value (orthogonalized on Legal_System due to high correlation). GDP_Per_Capita = GDP per capita, GDP_Per_Capita_Growth =
Annual growth rate of GDP_Per_Capita.
In columns (1) to (6) credit risk of Islamic banks is compared to conventional banks, while in each stage a set of control variables is included into the model. In
column (7), the interaction term of Islamic_Bank and Muslim_Share (Islamic_Bank × Muslim_Share) is included to analyze the possible impact of clients’
religiosity on credit risk of Islamic banks. In column (8), we replace Muslim_Share and Islamic_Bank × Muslim_Share with Legal_System and Islamic_Bank ×
Legal_System respectively for further analysis of the possible impact of clients’ religiosity on credit risk of Islamic banks. In column (9), we investigate whether
credit risk of Islamic banks is more or less sensitive to domestic interest rate compared to conventional banks, by adding the interaction term of Islamic_Bank
and Domestic_Interest_Rate (Islamic_Bank × Domestic_Interest_Rate). In column (10), we add the interaction of Islamic_Bank and Capital_Asset_Ratio
(Islamic_Bank × Capital_Asset_Ratio) to understand whether leverage can discipline Islamic banks more effectively than conventional banks. In order to
investigate whether size has different effect on credit risk of Islamic banks compared to conventional banks, in column (11) the interaction of Islamic_Bank and
Size (Islamic_Bank × Size) is added to the model.
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Robust
z-statistics are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
58
Table IV. Insolvency Risk Model
This table presents the estimation of the insolvency risk model. In columns (1) to (8), we investigate whether insolvency risk of Islamic banks is, on average,
higher or lower than conventional banks. In the last four columns we include various interaction variables highlighting whether religious factors influence
insolvency risk
(1)
Zscore_rw
(2)
Zscore_rw
(3)
Zscore_rw
(4)
Zscore_rw
(5)
Zscore_rw
(6)
Zscore_rw
(7)
Zscore_P1_rw
(8)
Zscore_P2_rw
(9)
Zscore_rw
(10)
Zscore_rw
(11)
Zscore_rw
(12)
Zscore_rw
Islamic_Bank (β1)
-0.115
(-0.89)
-0.070
(-0.58)
-0.102
(-0.82)
-0.059
(-0.47)
-0.114
(-0.92)
0.088
(0.57)
0.054
(0.46)
0.118
(0.74)
-0.791
(-1.07)
0.153
(0.65)
0.514
(0.62)
0.263
(0.88)
Islamic_Window_Bank (β2)
0.200
(1.37)
0.140
(1.01)
-0.019
(-0.74)
0.107
(0.76)
-0.020
(-0.76)
0.113
(0.81)
-0.030
(-1.09)
-0.039
(-0.27)
-0.032
(-1.23)
0.198
(1.16)
-0.050
(-1.63)
0.310**
(2.12)
0.064**
(2.41)
0.244
(1.42)
-0.069**
(-2.21)
-0.043
(-0.30)
-0.038
(-1.41)
0.201
(1.17)
-0.051*
(-1.65)
0.193
(1.13)
-0.044
(-1.27)
0.196
(1.15)
-0.049
(-1.61)
Variables
Size (β3)
Islamic_Bank × Size (βIS)
-0.031
(-0.53)
Market_Share (β4)
-0.028
(-0.77)
0.002
(1.03)
Loan_Total_Earning_Asset_Ratio (β5)
-0.032
(-0.87)
0.002
(0.99)
-0.024
(-0.65)
0.002
(0.91)
-0.017
(-0.45)
0.001
(0.68)
-0.045
(-0.64)
0.001
(0.49)
-0.045
(-0.65)
0.002
(0.87)
-0.050
(-0.66)
0.001
(0.36)
-0.005
(-0.12)
0.001
(0.60)
-0.042
(-0.57)
0.001
(0.50)
-0.051
(-0.70)
0.001
(0.48)
Islamic_Bank ×
Loan_Total_Earning_Asset_Ratio (βIL)
-0.003
(-0.68)
Asset_Growth (β6)
-0.000
(-0.17)
-0.005**
(-2.49)
-0.010***
(-6.81)
Noninterest_Income (β7)
Cost_Inefficiency (β8)
State_Bank (β9)
Foreign_Bank (β10)
Subsidiary (β11)
-0.000
(-0.22)
-0.005**
(-2.43)
-0.010***
(-6.78)
0.000
(0.02)
-0.005**
(-2.45)
-0.010***
(-6.72)
0.000
(0.32)
-0.006***
(-2.91)
-0.009***
(-5.78)
0.001
(0.67)
-0.005**
(-2.23)
-0.009***
(-5.69)
0.000
(0.12)
-0.006***
(-2.74)
-0.018***
(-8.00)
0.001
(0.77)
-0.005**
(-2.35)
-0.008***
(-5.30)
0.000
(0.27)
-0.006***
(-2.87)
-0.009***
(-5.83)
0.001
(0.68)
-0.005**
(-2.20)
-0.009***
(-5.69)
0.001
(0.66)
-0.005**
(-2.23)
-0.009***
(-5.63)
0.001
(0.72)
-0.005**
(-2.22)
-0.009***
(-5.68)
-0.041
(-0.30)
-0.058
(-0.37)
-0.299***
(-2.69)
-0.063
(-0.45)
-0.076
(-0.50)
-0.298***
(-2.66)
-0.006
(-0.04)
-0.087
(-0.57)
-0.357***
(-3.27)
0.156
(1.26)
-0.158
(-1.03)
-0.318***
(-2.63)
0.062
(0.51)
-0.114
(-0.78)
-0.205
(-1.50)
0.175
(1.31)
-0.128
(-0.81)
-0.329**
(-2.50)
-0.005
(-0.03)
-0.111
(-0.72)
-0.366***
(-3.35)
0.154
(1.25)
-0.157
(-1.03)
-0.316***
(-2.60)
0.162
(1.30)
-0.157
(-1.03)
-0.315***
(-2.60)
0.157
(1.27)
-0.149
(-0.97)
-0.317***
(-2.62)
-0.393**
(-2.03)
-0.209
(-1.58)
-0.389**
(-1.97)
-0.203
(-1.60)
-0.348*
(-1.65)
-0.160
(-1.23)
-0.771***
(-3.04)
0.009
(0.07)
-0.246
(-1.21)
-0.177
(-1.41)
-0.404**
(-1.98)
-0.203
(-1.60)
-0.347*
(-1.65)
-0.162
(-1.24)
-0.358*
(-1.68)
-0.157
(-1.21)
-0.353*
(-1.69)
-0.161
(-1.24)
-0.047***
(-4.94)
-0.048***
(-4.97)
-0.047***
(-4.98)
Young_Bank (β12)
Middle_Aged_Bank (β13)
Muslim_Share (β14)
-0.011***
(-3.11)
-0.013***
(-3.34)
Islamic_Bank × Muslim_Share (βIM)
Domestic_Interest_Rate (β15)
-0.042***
(-7.55)
-0.048***
(-4.97)
-0.037***
(-3.79)
-0.048***
(-5.86)
0.008
(0.91)
-0.043***
(-7.22)
Islamic_Bank × Domestic_Interest_Rate
(βID)
-0.012
(-0.35)
HHI (β16)
GDP_Per_Capita (β17)
GDP_Per_Capita_Growth (β18)
Constant (β0)
Year Dummies
Country Dummies
Number of Obs
R-squared
-0.049
(-0.69)
0.002
(0.72)
-0.563
(-1.45)
-0.949*
(-1.75)
-0.796
(-1.44)
-1.144**
(-2.02)
-0.502
(-1.23)
-0.956*
(-1.75)
-0.936*
(-1.73)
-0.935*
(-1.72)
-0.005*
(-1.66)
-0.066***
(-2.81)
-0.049***
(-2.78)
-0.075***
(-3.08)
-0.006*
(-1.86)
-0.065***
(-2.80)
-0.066***
(-2.80)
-0.065***
(-2.79)
-0.006
(-0.59)
5.943***
(9.96)
0.002
(0.13)
2.080***
(3.86)
-0.009
(-0.86)
6.191***
(10.14)
-0.014
(-1.57)
6.343***
(13.40)
-0.006
(-0.58)
5.943***
(9.96)
-0.006
(-0.59)
5.866***
(9.09)
-0.006
(-0.63)
5.882***
(9.72)
3.399***
(61.32)
4.315***
(10.89)
4.400***
(11.00)
4.560***
(10.99)
-0.015*
(-1.71)
6.112***
(13.55)
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
2,129
0.001
1,910
0.040
1,910
0.043
1,910
0.047
1,910
0.097
1,910
0.161
1,813
0.226
1,931
0.176
1,910
0.103
1,910
0.161
1,910
0.161
1,910
0.161
H0: β 14 = β IM = 0 (F-stat.)
H0: β 14 + β IM = 0 (F-stat.)
H0: β 15 = β ID = 0 (F-stat.)
H0: β 15 + β ID = 0 (F-stat.)
H0: β 3 = β IS = 0 (F-stat.)
H0: β 3 + β IS = 0 (F-stat.)
11.4***
0.41
24.78***
2.87*
3.6
2.23
H0: β 5 = β IS = 0 (F-stat.)
H0: β 5 + β IS = 0 (F-stat.)
0.67
0.14
Dependent Variables: Zscore_rw = Logarithm of rolling-window Zscore which is equal to (ROAA+ Capital_Asset_Ratio)/SDROAA_rw, ROAA = Return on average assets,
Capital_Asset_Ratio = Equity capital to asset ratio, SDROAA_rw = Standard deviation of ROAA over 3 years (current year and two previous consecutive years). Banks need to have
three consecutive observations. Acquiring banks are excluded from the sample, since the volatility on their assets returns can be due to the acquisition. Zscore_P1_rw = Logarithm of
ROAA/SDROAA3_rw, Zscore_P2_rw = Logarithm of Capital_Asset_Ratio/SDROAA3_rw.
Variable definitions: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets, Islamic_Bank × Size = Interaction
term of Islamic_Bank and Size, Market_Share = Logarithm of market share of total assets, orthogonalized on Size, Loan_Total_Earning_Asset_Ratio = Share of net loans in total earning
assets, Islamic_Bank × Loan_Total_Earning_Asset_Ratio = Interaction term of Islamic_Bank and Loan_Total_Earning_Asset_Ratio, Asset_Growth = Annual growth rate of total assets,
Noninterest_Income = Share of non-interest income in total operating income, Cost_Inefficiency = Cost to income ratio, State_Bank = State-owned bank dummy, Foreign_Bank =
Foreign-owned bank dummy, Subsidiary = Subsidiary dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank = Middle-aged bank dummy, Muslim_Share = Share of
Muslims in population, Islamic_Bank × Muslim_Share = Interaction term of Islamic_Bank and Muslim_Share, Domestic_Interest_Rate = Domestic interest rate, Islamic_Bank ×
Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = Hirschman-Herfindahl index, GDP_Per_Capita = GDP per capita,
59
GDP_Per_Capita_Growth = Annual growth rate of GDP_Per_Capita.
In columns (1) to (6) insolvency risk of Islamic banks is compared to conventional banks, while in each stage a set of control variables is included into the model. In columns (7) and
(8), insolvency risk proxy is replaced by Zscore_P1_rw and Zscore_P2_rw, respectively. In column (9), the interaction term of Islamic_Bank and Muslim_Share (Islamic_Bank ×
Muslim_Share) is added to analyze the possible impact of clients’ religiosity on insolvency risk of Islamic banks. In column (10), we investigate whether insolvency risk of Islamic
banks is more or less sensitive to domestic interest rate compared to conventional banks, by adding the interaction term of Islamic_Bank and Domestic_Interest_Rate (Islamic_Bank ×
Domestic_Interest_Rate). In order to understand whether size has different effect on insolvency risk of Islamic banks compared to conventional banks, in column (11) the interaction of
Islamic_Bank and Size (Islamic_Bank × Size) is added to the model. We add the interaction term of Islamic_Bank and Loan_Total_Earning_Asset_Ratio (Islamic_Bank ×
Loan_Total_Earning_Asset_Ratio) in column (12) to investigate whether the composition of total earning assets can have a significantly different effect on Islamic banks’ stability compared
to conventional banks.
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Robust z-statistics are
reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.
60
Table V. Bank Interest Rate Model
This table presents the results of the bank interest rate model. In the first five columns, we investigate whether interest rate proxies of Islamic banks are,
on average, higher or lower than conventional banks. In columns (6) to (10), the sensitivity of interest income and expense of Islamic banks to domestic
interest rate are analysed.
(1)
Net_Interest_Margin
(A)
(2)
Interest_Income_Rate
(B)
(3)
Interest_Expense_Rate
(C)
(4)
Loan_Rate
(D)
(5)
Deposit_Rate
(E)
(6)
(7)
(8)
(9)
A
B
C
D
E
Islamic_Bank (γ 1)
0.249
(0.97)
-0.487
(-1.44)
-0.125
(-0.47)
-0.479
(-0.87)
-0.228
(-0.60)
0.346
(1.03)
0.752*
(1.77)
0.723*
(1.88)
0.317
(0.44)
0.129
(0.31)
Islamic_Window_Bank (γ 2)
0.189
(0.86)
-0.051
(-1.08)
0.051
(0.41)
0.036
(0.12)
-0.097
(-1.40)
0.201
(1.24)
-0.132
(-0.72)
0.064
(0.98)
-0.317
(-1.42)
0.235
(0.41)
-0.047
(-0.38)
-0.769
(-1.47)
-0.007
(-0.02)
-0.056
(-0.65)
-0.193
(-0.64)
0.198
(0.89)
-0.052
(-1.11)
0.055
(0.45)
0.128
(0.42)
-0.115*
(-1.66)
0.251
(1.55)
-0.072
(-0.40)
0.052
(0.83)
-0.285
(-1.34)
0.312
(0.54)
-0.062
(-0.52)
-0.713
(-1.44)
0.025
(0.07)
-0.063
(-0.76)
-0.165
(-0.56)
0.016***
(3.15)
-0.033***
(-6.80)
-0.013***
(-4.63)
-0.001
(-0.13)
-0.021***
(-3.54)
-0.008***
(-2.79)
-0.005
(-0.98)
0.007*
(1.65)
-0.000
(-0.09)
-0.015
(-1.38)
0.005
(0.66)
0.002
(0.30)
-0.012
(-1.47)
0.001
(0.19)
0.004
(0.76)
0.016***
(3.14)
-0.033***
(-6.80)
-0.013***
(-4.65)
-0.002
(-0.26)
-0.021***
(-3.46)
-0.008***
(-2.63)
-0.006
(-1.09)
0.007*
(1.76)
0.000
(0.11)
-0.016
(-1.51)
0.006
(0.78)
0.002
(0.26)
-0.013
(-1.57)
0.002
(0.24)
0.004
(0.76)
-0.003
(-0.45)
-0.227
(-0.98)
-0.484*
(-1.89)
-0.010
(-1.08)
-0.085
(-0.23)
-1.237***
(-3.26)
-0.003
(-0.49)
0.187
(0.51)
-0.308
(-0.97)
-0.010
(-0.76)
0.087
(0.15)
-0.647
(-0.52)
0.009
(0.89)
-0.274
(-0.56)
0.312
(0.70)
-0.003
(-0.46)
-0.229
(-0.98)
-0.483*
(-1.89)
-0.011
(-1.19)
-0.116
(-0.31)
-1.245***
(-3.35)
-0.003
(-0.57)
0.172
(0.47)
-0.323
(-1.00)
-0.011
(-0.83)
0.059
(0.10)
-0.598
(-0.49)
0.008
(0.86)
-0.286
(-0.58)
0.312
(0.69)
0.264
(1.24)
0.537
(1.50)
0.408**
(1.96)
-0.737**
(-2.39)
-0.348
(-0.79)
0.280
(0.97)
-0.745***
(-3.65)
-0.466
(-1.52)
-0.130
(-0.60)
-0.751
(-1.37)
1.530
(0.86)
0.780*
(1.72)
-0.740**
(-2.33)
1.412*
(1.76)
0.607*
(1.95)
0.269
(1.26)
0.539
(1.50)
0.407*
(1.95)
-0.712**
(-2.31)
-0.332
(-0.74)
0.261
(0.89)
-0.726***
(-3.57)
-0.459
(-1.47)
-0.154
(-0.71)
-0.700
(-1.28)
1.490
(0.84)
0.739
(1.62)
-0.715**
(-2.26)
1.391*
(1.74)
0.595*
(1.91)
0.055***
(2.77)
0.176***
(8.12)
0.101***
(8.97)
0.071
(1.09)
0.180***
(4.13)
0.054***
(2.77)
0.176***
(8.12)
0.101***
(8.93)
0.076
(1.16)
0.181***
(4.14)
-0.016
(-0.32)
-0.198***
(-3.03)
-0.138***
(-2.65)
-0.139
(-1.50)
-0.058
(-1.10)
Variables
Size (γ3)
Market_Share (γ 4)
Capital_Asset_Ratio (γ5)
Noninterest_Income (γ6)
Cost_Inefficiency (γ 7)
Loan_Loss_Reserve (γ8)
State_Bank (γ9)
Foreign_Bank (γ 10)
Subsidiary (γ 11)
Young_Bank (γ12)
Middle_Aged_Bank (γ13)
Domestic_Interest_Rate (γ15)
Islamic_Bank ×
Domestic_Interest_Rate (γID)
HHI (γ16)
GDP_Per_Capita (γ17)
GDP_Per_Capita_Growth (γ18)
Constant (γ0)
Number of Obs
R-squared
(10)
-0.327
(-0.38)
-2.373**
(-2.20)
-0.129
(-0.14)
-0.655
(-0.19)
-4.143**
(-2.23)
-0.340
(-0.40)
-2.464**
(-2.29)
-0.171
(-0.19)
-0.670
(-0.19)
-4.193**
(-2.27)
-0.016
(-0.87)
-0.002
(-0.10)
4.651***
(5.64)
-0.009
(-0.35)
0.030
(1.16)
9.035***
(7.36)
0.014
(0.68)
0.048*
(1.85)
2.540**
(2.14)
0.002
(0.05)
-0.009
(-0.24)
6.164***
(2.84)
-0.032
(-1.12)
0.048*
(1.78)
2.001
(1.33)
-0.016
(-0.87)
-0.002
(-0.09)
4.657***
(5.64)
-0.006
(-0.24)
0.031
(1.19)
9.085***
(7.52)
0.016
(0.75)
0.049*
(1.87)
2.563**
(2.23)
0.006
(0.13)
-0.009
(-0.24)
6.144***
(2.93)
-0.032
(-1.09)
0.048*
(1.76)
2.005
(1.38)
2,269
0.506
2,258
0.611
2,220
0.534
715
0.557
643
0.656
2,269
0.506
2,258
0.614
2,220
0.535
715
0.559
643
0.658
7.67**
0.58
71.39***
0.11
86.82***
0.48
3.20
0.35
18.34***
3.20*
H0: γ 15 = γ ID = 0 (F-stat.)
H0: γ 15 + γ ID = 0 (F-stat.)
Dependent Variables: Net_Interest_Margin = (Interest Income – Interest Expense) / Average Earning assets, Interest_Income_Rate = Interest income divided by average earning
assets for conventional banks and mark-up income over average earning assets for Islamic banks, Interest_Expense_Rate = Interest expense divided by average interest bearing
liabilities and profit payouts over average profit bearing liabilities for Islamic banks, Loan_Rate = Interest income on loans divided by average gross lending for conventional
banks and mark-up income on lending divided by average gross loans for Islamic banks, Deposit_Rate = Interest expense on customer deposit divided by average customer
deposits for conventional banks and profit payouts on customer deposits divided by average customer deposits for Islamic banks.
Explanatory Variables: Islamic_Bank = Islamic bank dummy, Islamic_Window_Bank = Islamic window bank dummy, Size = Logarithm of total assets, Market_Share =
Logarithm of market share of total assets, orthogonalized on Size, Capital_Asset_Ratio = The ratio of equity capital on total assets, Noninterest_Income = Share of non-interest
income in total operating income, Cost_Inefficiency = Cost to income ratio, Loan_Loss_Reserve = Loan loss reserves on gross loans ratio, State_Bank = State-owned bank
dummy, Foreign_Bank = Foreign-owned bank dummy, Subsidiary = Subsidiary dummy, Young_Bank = Young bank dummy, Middle_Aged_Bank = Middle-aged bank dummy,
Domestic_Interest_Rate = Domestic interest rate, Islamic_Bank × Domestic_Interest_Rate = Interaction term of Islamic_Bank and Domestic_Interest_Rate, HHI = HirschmanHerfindahl index, GDP_Per_Capita = GDP per capita, GDP_Per_Capita_Growth = Annual growth rate of GDP_Per_Capita.
In columns (1) to (5), interest rate proxies (Net_Interest_Margin, Interest_Income_Rate, Interest_Expense_Rate, Loan_Rate and Deposit_Rate) of Islamic banks are compared
to those of conventional banks. In columns (6) to (10), we add the interaction term of Islamic_Bank and Domestic_Interest_Rate (Islamic_Bank × Domestic_Interest_Rate) to
investigate whether interest income and expense of Islamic banks are more or less sensitive to domestic interest rate compared to conventional banks.
We apply random effect technique with robust standard errors for our estimations. All the accounting and macro level variables are lagged for one period. Year and country
dummies are included in the model, but not reported in the table. Robust z-statistics are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10%
respectively.
61